Currently tracking 241 active AI roles, down 26% versus the prior 4 weeks. Primary focus: Serve · Engineering. Salary range $123k–$392k (avg $231k).
Capital One currently has 293 active AI-related job listings. The majority of these roles are focused on serving infrastructure, accounting for 28% of the total, followed closely by agents at 26% and post-training at 23%. Engineering is the dominant function, with 234 roles, and hiring is primarily concentrated in the United States. Frequent tech tags include model_serving, vector_db, and llm_observability, suggesting a focus on the operational aspects of AI deployment. In the last 30 days, Capital One posted 124 new AI roles, representing a 22% increase compared to the previous 30-day period.
Capital One currently has 305 active AI-related roles in our index. The most common open titles are: Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (9), Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) (8), Applied Researcher I (6), Distinguished Engineer (6), Applied Researcher II (5). Most positions are in Engineering and Research.
Capital One's active AI hiring is concentrated in: serving infrastructure (28%), agents (27%), post-training (23%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Capital One is hiring AI talent in: United States (299 roles), United Kingdom (3 roles), Canada (2 roles), Philippines (1 role).
Job postings at Capital One most frequently reference: model serving, vector db, fine tuning, llm observability, inference infra.
In the past 30 days, Capital One has posted 96 new AI-related roles. That is a -26% change versus the prior 30 days (130 → 96).
| Title | Stage | AI score |
|---|---|---|
| Applied Researcher II, AI Foundations Applied Researcher II focused on AI Foundations, responsible for building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves partnering with cross-functional teams to deliver AI-powered products and engaging in applied research to push state-of-the-art AI into customer experiences. Requires experience in building large deep learning models, training optimization, self-supervised learning, robustness, explainability, or RLHF, with a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. |
| Post-trainPretrain |
| 9 |
| Applied Researcher II Applied Researcher II role at Capital One focused on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to create next-generation customer experiences and delivering models at scale. Requires a strong technical background in deep learning, model optimization, and experience with open-source tools and cloud platforms. | Post-trainPretrain | 9 |
| Principal Associate, Data Science - AI Foundations This role focuses on research and development of GenAI powered conversational capabilities and scalable futuristic solutions for customer digital experience and real-time support. The primary focus is on fine-tuning LLMs for domain-specific conversational applications, inference optimization, and multi-agentic workflows, with a secondary focus on building these agentic systems. | Post-trainAgent | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher I role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within Capital One's fintech domain. The role involves partnering with cross-functional teams to deliver AI-powered products, building AI foundation models through all development phases, and conducting applied research to enhance customer experiences. Requires a strong technical background in deep learning, model training, and experience with open-source tools and cloud platforms. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher II Applied Researcher II role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with significant experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Senior Director, Applied Research Senior Director of Applied Research leading teams to drive strategic direction in AI at Capital One, focusing on building AI foundation models from design through training, evaluation, validation, and implementation, and engaging in high-impact applied research to create next-generation customer experiences. The role involves people management, external representation in the research community, and leveraging a broad stack of technologies for AI-powered products in fintech. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Core and Agentic AI) Applied Researcher I focused on AI Foundations, LLM Core, and Agentic AI at Capital One. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like PyTorch, AWS, Huggingface, and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting applied research to create next-generation customer experiences. Requires a PhD or MS with experience in AI/ML, with a strong understanding of AI methodologies, experience building large deep learning models (language, images, events, graphs), and expertise in optimization, self-supervised learning, robustness, explainability, or RLHF. An engineering mindset with a track record of delivering models at scale and experience in delivering libraries or platform code is essential. A track record of high-quality ideas or improvements demonstrated by publications or projects is also required. | Post-trainAgent | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning) Applied Researcher role focused on AI Foundations, LLM Customization, Finetuning, and Reinforcement Learning within a fintech company. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to improve customer experiences and requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models, engaging in applied research to improve customer experiences, and delivering AI-powered products. The role involves training optimization, self-supervised learning, robustness, explainability, and RLHF, with an emphasis on delivering models at scale. | Post-trainPretrain | 9 |
| Applied Researcher II This role is for an Applied Researcher II at Capital One focused on building AI foundation models and applying state-of-the-art AI to customer-facing products. The role involves research, development, training, evaluation, and implementation of AI models, with a strong emphasis on pushing AI capabilities into next-generation customer experiences. The candidate will work with cross-functional teams and leverage various technologies including Pytorch, AWS, Huggingface, and VectorDBs. Experience in training optimization, self-supervised learning, robustness, explainability, RLHF, and delivering models at scale is required. A PhD or MS in a related field with significant research experience is preferred, along with a publication record. | Post-trainPretrain | 9 |
| Applied Researcher II (AI Foundations, LLM Core and Agentic AI) Applied Researcher II at Capital One focused on AI Foundations, LLM Core, and Agentic AI. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and engaging in applied research to advance customer experiences. The ideal candidate has a deep understanding of AI methodologies, experience building large deep learning models (language, images, events, graphs), expertise in optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLMs, including pre-training and fine-tuning, is highly preferred. | Post-trainAgent | 9 |
| Applied Researcher I (AI Foundations) Applied Researcher I (AI Foundations) at Capital One, focusing on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves applied research to push state-of-the-art AI into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, deep learning, and delivering models at scale. | Post-trainPretrain | 9 |
| Sr. Distinguished Applied Researcher Sr. Distinguished Applied Researcher at Capital One focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to integrate the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. This individual contributor role requires guiding and mentoring teams, representing Capital One in the research community, and delivering AI-powered products and platforms. | Post-trainPretrain | 9 |
| Senior Associate, Data Scientist - NLP Senior Associate Data Scientist focused on NLP and LLMs for a financial services company's mobile app. The role involves building, adapting, and fine-tuning LLMs for customer-facing features, operationalizing models in production systems, and leveraging technologies like PyTorch, Hugging Face, LangChain, and VectorDBs. The position requires experience in model development phases from design to validation and operationalization at scale for a large customer base. | Post-trainServe | 8 |
| Senior Manager, Data Science - AI Foundations Senior Manager, Data Science - AI Foundations at Capital One. This role focuses on building and shipping AI/ML solutions for the company's mobile app, leveraging technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. The position involves adapting and fine-tuning LLMs for customer-facing applications, building ML and NLP models through all development phases, and operationalizing them in production systems serving over 80 million customers. The ideal candidate has experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF, with a track record of delivering models at scale. | Post-trainServe | 8 |
| Senior Associate, Data Scientist - NLP Senior Associate Data Scientist focused on NLP and LLMs for a financial services company's mobile app. The role involves building, adapting, and fine-tuning LLMs for customer-facing features, operationalizing models in production systems, and leveraging technologies like PyTorch, Hugging Face, LangChain, and VectorDBs. The position requires experience in model development phases from design to validation and operationalization at scale for a large customer base. | Post-trainServe | 8 |
| Principal Associate, Data Scientist - LLM Customization Team This role focuses on customizing and fine-tuning LLMs for specific business applications within a financial services company. The data scientist will work on building NLP models through all phases of development, from design through training, evaluation, and validation, and operationalizing them in production systems. The role involves leveraging technologies like Pytorch, Hugging Face, LangChain, and VectorDBs, and requires experience in training language models, adaptation, fine-tuning, and potentially RLHF. | Post-trainServe | 8 |
| Senior Data Scientist, AI Foundations Senior Data Scientist focused on building and shipping AI/ML solutions for a mobile app, including adapting and fine-tuning LLMs for customer-facing applications. The role involves building ML and NLP models through all development phases, from design to training, evaluation, and validation, and operationalizing them in production systems serving millions of customers. Experience with LLMs, NLP, training language models, and delivering models at scale is required. | Post-trainServe | 8 |
| Senior Manager, Data Science - LLM Customization Team Senior Manager of Data Science focused on LLM customization within Capital One's AI Foundations team. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like PyTorch, Hugging Face, LangChain, and VectorDBs. Responsibilities include adapting and fine-tuning LLMs for business applications, building NLP models through development phases, and operationalizing them in production systems. The ideal candidate has experience in training language models, expertise in areas like self-supervised learning or RLHF, and a track record of delivering models at scale. | Post-trainServe | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into next-generation customer experiences, leveraging technologies like Pytorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. Experience with training optimization, self-supervised learning, robustness, explainability, and RLHF is desired, with a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models and delivering AI-powered products, leveraging state-of-the-art AI developments for customer experiences. The role involves research, training, evaluation, and implementation of large deep learning models, with a focus on optimization, self-supervised learning, robustness, explainability, and RLHF. | Post-trainPretrain | 8 |
| Principal Associate, Data Scientist - LLM Customization Team Capital One is seeking a Principal Associate Data Scientist to join their LLM Customization Team. This role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch, Hugging Face, LangChain, and VectorDBs. The primary focus is on adapting and fine-tuning LLMs for business-specific applications, building NLP models through all phases of development, and operationalizing them in production systems. | Post-trainServe | 8 |
| Applied Researcher II (AI Foundations, LLM Core and Agentic AI) Applied Researcher II focused on AI Foundations, LLM Core, and Agentic AI at Capital One. The role involves partnering with cross-functional teams to deliver AI-powered products, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting high-impact applied research to improve customer experiences. The ideal candidate has a strong technical background, experience building large deep learning models, expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. Experience with LLM pre-training, optimization, or fine-tuning is highly preferred. | Post-trainAgent | 8 |
| Applied Researcher II Applied Researcher II at Capital One focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires experience building large deep learning models and delivering models at scale. | Post-trainServe | 8 |
| Applied Researcher I Applied Researcher I at Capital One focused on building AI foundation models and delivering them at scale for customer-facing products. The role involves partnering with cross-functional teams, leveraging various technologies, and conducting applied research to push AI capabilities into next-generation customer experiences. Requires a strong technical background in deep learning, model training, optimization, and a track record of delivering models in production. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models, engaging in applied research to push AI developments into customer experiences, and delivering models at scale. Requires experience in training optimization, self-supervised learning, robustness, explainability, or RLHF, with a track record of delivering libraries or platform code. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push state-of-the-art AI into customer experiences, with a strong emphasis on delivering models at scale and potentially contributing to publications. | Post-trainPretrain | 8 |
| Applied Researcher II Applied Researcher II at Capital One focused on building AI foundation models and delivering them at scale for customer-facing products. The role involves partnering with cross-functional teams, leveraging technologies like Pytorch and VectorDBs, and engaging in applied research to push AI capabilities. | Post-trainPretrain | 8 |
| Applied Researcher I Applied Researcher I role focused on building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. The role involves high-impact applied research to push the latest AI developments into customer experiences, leveraging technologies like Pytorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with experience in AI/ML, with expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Applied Researcher II Applied Researcher II at Capital One focused on partnering with cross-functional teams to deliver AI-powered products. The role involves leveraging technologies like Pytorch and VectorDBs, building AI foundation models through all development phases, and engaging in applied research to advance customer experiences. Requires a deep understanding of AI methodologies, experience building large deep learning models, and a track record of delivering models at scale. | Post-trainPretrain | 8 |
| Senior Lead AI Engineer (LLM Customization and Finetuning) Senior Lead AI Engineer focused on LLM customization and finetuning within Capital One's Intelligent Foundations and Experiences (IFX) team. The role involves designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It requires leveraging AI technologies like Huggingface, VectorDBs, and PyTorch, and optimizing LLM performance for scalability, cost, latency, and throughput in production AI systems. The candidate should have a strong engineering and mathematics foundation, experience with cloud platforms, and the ability to lead and mentor teams. | Post-trainServe | 8 |
| Manager, Data Science - AI Foundations Manager, Data Science - AI Foundations role at Capital One focuses on building and shipping AI/ML solutions for customer-facing applications, including personalization and digital assistants. The role involves leveraging LLMs, fine-tuning them, and building ML/NLP models through all development phases, from design to production operationalization. It emphasizes partnering with cross-functional teams and utilizing technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs. | Post-trainServe | 8 |
| Principal Data Scientist, AI Foundations This role focuses on building and shipping AI/ML solutions for Capital One's mobile app, leveraging LLMs and generative AI. The Principal Data Scientist will partner with cross-functional teams to deliver AI-powered products, adapt and fine-tune LLMs for customer-facing applications, and build ML/NLP models through all phases of development, including training, evaluation, and validation, with a strong emphasis on operationalizing them in production systems serving millions of customers. Experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF are required, along with a track record of delivering models at scale. | Post-trainServe | 8 |
| Senior Manager, Data Science - AI Foundations Senior Manager, Data Science - AI Foundations role at Capital One, focusing on building and shipping AI/ML solutions for their mobile app. The role involves partnering with cross-functional teams, leveraging technologies like PyTorch, AWS, Hugging Face, LangChain, and VectorDBs, and specializing in NLP and LLMs for customer-facing applications. Responsibilities include model development from design to production, operationalization, and translating complex work into business goals. The ideal candidate is customer-first, innovative, creative, a leader, technical with hands-on LLM experience, and influential. Experience in training language models, computer vision models, and expertise in areas like training optimization, self-supervised learning, explainability, and RLHF is required, along with a track record of delivering models at scale in both training and inference. | Post-trainServe | 8 |
| Manager, Data Scientist - Business Cards & Payments Credit Infrastructure Team Capital One is seeking a Manager, Data Scientist for their Business Cards & Payments Credit Infrastructure Team. This role involves building machine learning models for credit card acquisitions valuations, supporting credit programs through robust models and tools. The candidate will partner with cross-functional teams, leverage technologies like Python, AWS, and Spark, and translate complex work into business goals. The ideal candidate is customer-focused, creative, a leader, technically proficient with open-source tools and cloud platforms, and statistically minded with experience in various modeling techniques. | Post-train | 7 |
| Principal, Data Scientist - Card Intelligence This role focuses on building and deploying machine learning models for the credit card lifecycle, including marketing, underwriting, and fraud prevention. The candidate will work with a cross-functional team, leverage technologies like Python and AWS, and be responsible for all phases of model development from design to implementation. The role requires a strong statistical background and experience with various machine learning techniques. | Post-train | 7 |
| Senior Associate, Data Scientist - Card Intelligence Data Scientist role focused on building and deploying machine learning models for the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. The role involves leveraging customer data, applying various ML techniques, and working with technologies like Python, AWS, and Spark. | Post-train | 7 |
| Senior Manager, Data Science, Bank Operations Senior Manager, Data Science for Bank Operations at Capital One. This role involves building machine learning models for core internal capabilities like Check/Document Reading, Anomaly Identification, NLP of Calls, and operational forecasting, utilizing technologies such as neural networks, LLMs, and transformer architectures. The candidate will partner with cross-functional teams, leverage technologies like Python, AWS, H2O, and Spark, and manage the full lifecycle of ML model development. | Post-train | 7 |
| Principal Associate, Data Scientist - Bank Operations Data Science The Bank Operations Data Science team builds machine learning models for internal capabilities like Check/Document Reading, Anomaly Identification, Natural Language Processing of Calls, and operational forecasting, using technologies like neural networks, LLMs, and transformer architectures. The role involves partnering with cross-functional teams, leveraging technologies like Python and Snowflake, and building ML models through all phases of development. The ideal candidate is innovative, creative, technical, and statistically-minded, with experience in various ML techniques and cloud platforms. Specific experience with LLM risk mitigation is preferred. | Post-train | 7 |
| Manager, Data Scientist - Partnerships Acquisitions This role is for a Manager, Data Scientist focused on Partnerships Acquisitions within Capital One's fintech domain. The primary responsibility is to lead the development of next-generation machine learning models for credit decisioning, including application approval/decline, product optimization, and customer valuation. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building models through all phases of development. The ideal candidate has a strong technical and statistical background with hands-on experience in data science solutions and machine learning. | Post-train | 7 |
| Principal Associate, Data Scientist - Partnerships Acquisitions Capital One is seeking a Principal Associate Data Scientist to join their Partnerships Acquisitions team. This role focuses on building and implementing machine learning models for credit decisioning, product optimization, and customer valuation within the fintech domain. The position requires a strong background in data science, machine learning, and statistical modeling, with experience in Python, AWS, and SQL. The candidate will work with a cross-functional team to deliver data-driven products and enhance decision accuracy and efficiency. | Post-train | 7 |
| Principal Associate, Data Scientist - Cash Flow Underwriting Data Scientist role focused on building machine learning models for cash flow underwriting in the fintech domain. The role involves leveraging advanced ML techniques to engineer predictive signals from banking and transaction data to forecast creditworthiness and customer behavior. It requires building models through all phases of development, from design to implementation, using technologies like Python, Spark, and AWS. | Post-train | 7 |
| Senior Associate, Data Scientist Data Scientist role focused on building machine learning models for Capital One's Commercial Bank Team, involving forecasting, analytical tools, and model implementation. The role requires leveraging technologies like Python, AWS, and Spark to analyze large datasets and deliver data-driven solutions. | Post-train | 7 |
| Senior Manager, Data Science - Anti-Money Laundering Modeling and Advanced Data Insights Capital One is seeking a Senior Manager of Data Science to lead the Anti-Money Laundering (AML) Modeling and Advanced Data Insights team. This role involves modernizing AML identification processes using advanced analytics, statistics, and machine learning. The team develops predictive models, monitoring dashboards, and reporting, with end-to-end responsibility for production models. The role requires partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building ML models through all development phases. | Post-train | 7 |
| Manager, Data Science - Consumer Identity Machine Learning Manager of Data Science focused on Consumer Identity Machine Learning within Capital One's AI foundation organization. The role involves building and operationalizing real-time, personalized ML models for customer experiences, fraud prevention, and data accuracy, serving over 50 million customers. It requires working across the data science lifecycle, from design and training to evaluation and productionization, in a regulated fintech environment. | Post-trainServe | 7 |
| Senior Associate, Data Scientist - US Card (Resiliency Intelligence) This role focuses on building and implementing supervised and reinforcement learning models to predict customer needs and recommend optimal financial solutions for customers facing hardship. The models directly impact millions of customers and the business's income, requiring development through all phases from design to implementation in production environments. | Post-train | 7 |