Capital One
Banking · Banking
- HQ
- McLean, US
- Founded
- 1994
- Website
- capitalone.com
Banking · Banking
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 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 |
| Sr. Distinguished AI Engineer (Agentic AI Platform) Senior Distinguished AI Engineer focused on building and scaling an Agentic AI Platform at Capital One. The role involves contributing to platform architecture, standardizing agentic workflows using frameworks like LangGraph and AutoGen, developing GenAI SDKs/CLIs, implementing central guardrail services for trust and safety, optimizing orchestration for cost reduction, and driving innovation in areas like multimodal RAG and hierarchical agent memory. The role also includes coaching and evangelizing the platform vision. | Agent | 9 |
| 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 and VectorDBs. Responsibilities include building AI foundation models through all development phases (design, training, evaluation, validation, implementation) and conducting high-impact 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 training optimization, self-supervised learning, robustness, explainability, or RLHF, and a track record of delivering models at scale. A PhD or MS with significant experience is required, with a focus on NLP, geometric deep learning, or optimization. | PretrainAgent | 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 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 |
| 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 (AI Foundations) Applied Researcher II (AI Foundations) at Capital One, focusing on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to develop next-generation customer experiences and requires experience with large deep learning models, training optimization, and delivering models at scale. The position is research-oriented within the fintech domain. | PretrainPost-train | 9 |
| Applied Researcher II (AI Foundations) Applied Researcher II focused on AI Foundations at Capital One, working on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to integrate AI developments into customer experiences and requires experience with large deep learning models, training optimization, and delivering models at scale. Collaboration with cross-functional teams and a strong understanding of AI methodologies are key. | PretrainPost-train | 9 |
| Applied Researcher I (AI Foundations) Applied Researcher I (AI Foundations) at Capital One, focusing on building AI foundation models from design through training, evaluation, validation, and implementation. The role involves applied research to push AI developments 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, with a strong understanding of AI methodologies and experience in training optimization, self-supervised learning, robustness, explainability, or RLHF. | PretrainPost-train | 9 |
| Distinguished Applied Researcher Distinguished Applied Researcher role focused on building AI foundation models from design through training, evaluation, validation, and implementation, and engaging in high-impact applied research to develop next-generation customer experiences. The role involves partnering with cross-functional teams and leveraging technologies like PyTorch, AWS, Huggingface, and VectorDBs. Requires a PhD or MS with significant experience in applied research, with a focus on large deep learning models, training optimization, self-supervised learning, robustness, explainability, or RLHF. Experience training large language models from scratch or through continued pre-training is highly preferred. | PretrainPost-train | 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 I (AI Foundations, LLM Core and Agentic AI) Applied Researcher I role 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 and VectorDBs, and building AI foundation models through all development phases. It emphasizes applied research to advance 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. | PretrainPost-train | 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 Director, Software Engineering - AI This role leads multiple teams of AI/ML software engineers to develop and manage enterprise LLM orchestration, generative AI pipelines, and low-latency inference microservices. It involves scaling production-grade ML systems and traditional architectures, mentoring engineers, and ensuring robust AI engineering practices for ethical deployment. | AgentServe | 8 |
| Senior Lead AI Engineer (GenAI Platform Services) This role focuses on designing, developing, testing, deploying, and supporting AI software components for GenAI Platform Services. It involves foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role also emphasizes optimizing large-scale production AI systems for performance (scalability, cost, latency, throughput) and contributing to the technical vision and roadmap of foundational AI systems. | ServeAgent | 8 |
| Lead AI Engineer (Vision model customization, VML) Lead AI Engineer focused on vision model customization and VML, responsible for designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves optimizing large-scale production AI systems for performance (scalability, cost, latency, throughput) and contributing to the technical vision and roadmap of foundational AI systems at Capital One, leveraging technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails. | ServeAgent | 8 |
| Lead Machine Learning Engineer (Manager IC) Lead Machine Learning Engineer at Capital One focused on building and productionizing foundation models using self-supervised learning for transformer architectures. The role involves large-scale training, representation learning, and serving models in production for applications like fraud, marketing, and servicing. Responsibilities include technical design, development, implementation, model/application code, ML architectural decisions, and ensuring high availability and performance. | PretrainServe | 8 |
| Senior Manager, AI Engineering (People Leader) (Gen AI Platform Services) Senior Manager of AI Engineering leading a team focused on building and deploying Gen AI Platform Services. The role involves overseeing the design, development, and support of AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, and observability. It also requires making build-vs-buy decisions, optimizing LLM performance, and contributing to the technical vision and roadmap for foundational AI systems. | ServeAgent | 8 |
| Senior Lead AI Engineer, Gen AI Platform This role focuses on engineering and optimizing large-scale production AI systems, specifically within the Generative AI Platform at Capital One. Responsibilities include designing, developing, testing, deploying, and supporting AI software components such as foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, and observability. The role also involves inventing and applying state-of-the-art LLM optimization techniques to improve performance (scalability, cost, latency, throughput) of these systems. The ideal candidate is deeply technical, experienced in AI/ML algorithms and technologies, and skilled in programming languages like Python, Go, Scala, or Java, with a strong foundation in engineering and mathematics. | ServeAgent | 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 |
| Manager, Data Science - GenAI Digital Assistant Manager, Data Science role focused on GenAI and conversational AI for a digital assistant, involving research, fine-tuning LLMs, inference optimization, and multi-agentic workflows within a fintech company. | AgentPost-train | 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 |
| Lead AI Engineer (Vision model customization, VLM) Lead AI Engineer focused on customizing vision models (VLMs) and optimizing large-scale AI systems, including foundation model training and LLM inference. The role involves designing, developing, testing, deploying, and supporting AI software components, leveraging technologies like AWS, Huggingface, VectorDBs, and Nemo Guardrails. Emphasis is placed on improving performance (scalability, cost, latency, throughput) of production AI systems and contributing to the technical vision for foundational AI systems. | ServePost-train | 8 |
| Lead Machine Learning Engineer Lead Machine Learning Engineer at Capital One, focused on building and deploying AI-powered risk management solutions. The role involves designing, developing, testing, and deploying AI software components, including LLM inference, similarity search, guardrails, governance, observability, and agentic AI. Responsibilities include fine-tuning, developing, and evaluating ML and foundation models, contributing to technical vision, and leveraging a broad stack of AI technologies. The role also requires retraining, maintaining, and monitoring production models, constructing optimized data pipelines, and ensuring responsible and explainable AI practices. | AgentPost-train | 8 |
| Manager, Data Scientist - Recommendation & Personalization Systems Manager, Data Scientist role focused on building and deploying personalized recommendation engines using Foundation Models, Reinforcement Learning, and Transformer-based architectures for a large-scale fintech company. The role involves partnering with cross-functional teams, leveraging technologies like Python, AWS, and Spark, and building ML models through all phases of development. | AgentPost-train | 8 |
| Lead Machine Learning Engineer (Manager IC) Lead Machine Learning Engineer at Capital One's Risk Tech division, focusing on building and deploying AI-powered risk management solutions. The role involves designing, developing, testing, deploying, and supporting AI software components, including fine-tuning models, managing LLM inference, similarity search, guardrails, governance, observability, and agentic AI. Responsibilities include contributing to the technical roadmap, leveraging AI technologies, informing ML infrastructure decisions, maintaining production models, and constructing data pipelines, with an emphasis on Responsible and Explainable AI. | AgentPost-train | 8 |
| Senior Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Senior Lead AI Engineer role focused on AI Foundations, LLM Core, and Agentic AI. Responsibilities include designing, developing, testing, deploying, and supporting AI software components such as foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch, and optimizing LLM performance for scalability, cost, latency, and throughput in production AI systems. | AgentServe | 8 |
| Lead AI Engineer (AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and optimizing AI systems, including foundation models, LLM inference, agentic AI, and related infrastructure. The role involves designing, developing, testing, deploying, and supporting AI software components, with a strong emphasis on improving performance, scalability, cost, and latency of large-scale production AI systems. It requires leveraging various AI technologies and contributing to the technical vision and roadmap for foundational AI systems. | AgentServe | 8 |
| Sr. Lead AI Engineer (Inference Optimization, FM hosting, AI Platform) This role focuses on optimizing the performance, scalability, cost, and latency of large-scale production AI systems, specifically for foundation model training and large language model inference. It involves designing, developing, and deploying AI software components, including inference services, and contributing to the AI platform. The role also touches upon aspects of foundation model training and agentic systems (via guardrails, similarity search). | ServeAgent | 8 |
| Director, Data Scientist Director of Data Science for the Generative AI Systems team at Capital One, focusing on building and delivering state-of-the-art generative AI solutions for internal efficiency and customer-facing applications. The role involves leading a team of NLP, speech, and computer vision specialists, experimenting with emerging generative AI technologies, and contributing to research. | ShipPost-train | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on LLM inference and optimization for AI systems within a large enterprise. The role involves designing, developing, and deploying AI software components, with a strong emphasis on improving the performance, scalability, cost, and latency of production AI systems. | Serve | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on LLM inference and optimization for AI systems within a large enterprise. The role involves designing, developing, and deploying AI software components, with a strong emphasis on improving the performance, scalability, cost, and latency of production AI systems. | Serve | 8 |
| Lead AI Engineer (GenAI Platform, AI Foundations, LLM Core and Agentic AI) Lead AI Engineer role focused on building and deploying GenAI platforms, LLM core, and agentic AI systems within an enterprise setting. Responsibilities include designing, developing, and supporting AI software components, optimizing LLM performance, and contributing to the technical vision for foundational AI systems. Requires experience with AI/ML algorithms, programming languages, and cloud platforms, with a focus on deploying scalable and responsible AI solutions. | AgentServe | 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 |
| Distinguished AI Engineer Distinguished AI Engineer role focused on designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves optimizing LLM performance (scalability, cost, latency, throughput) for large-scale production AI systems and contributing to the technical vision and roadmap of foundational AI systems. It requires strong engineering and mathematics foundations, expertise in Python/Go/Scala/Java, and experience with cloud platforms and AI technologies like Huggingface, VectorDBs, and PyTorch. | ServeAgent | 8 |
| Director, AI Engineering (Agentic AI Platform) Director of AI Engineering focused on building and managing an Agentic AI Platform. The role involves defining strategic vision, technical leadership for agentic workflows, overseeing the platform lifecycle, guiding technical teams, and ensuring scalability, reliability, and compliance. It requires strong leadership, technical expertise in AI/ML, and collaboration with cross-functional teams to deliver AI-powered products and foundational AI systems. | Agent | 8 |
| Senior Lead AI Engineer (Gen AI Platform Services) Senior Lead AI Engineer role focused on building and optimizing Gen AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, and governance. The role involves designing, developing, testing, deploying, and supporting AI software components, leveraging cloud platforms and AI technologies, and optimizing performance for scalability, cost, latency, and throughput. | ServeAgent | 8 |
| Senior Lead AI Engineer(MLX, Agentic AI, Gen AI platform Services) Senior Lead AI Engineer role focused on building and deploying AI-powered products and foundational AI systems at Capital One. Responsibilities include designing, developing, testing, deploying, and supporting AI software components such as foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role involves leveraging AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, and PyTorch, and optimizing LLM performance for scalability, cost, latency, and throughput. | ServeAgent | 8 |
| Senior Lead AI Engineer(MLX, Agentic AI, Gen AI platform Services) Senior Lead AI Engineer responsible for designing, developing, testing, deploying, and supporting AI software components including foundation model training, LLM inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role focuses on optimizing LLM performance for scalability, cost, latency, and throughput in production AI systems, leveraging technologies like AWS Ultraclusters, Huggingface, VectorDBs, and Nemo Guardrails. This role is part of the Intelligent Foundations and Experiences (IFX) team, aiming to advance AI science and engineering and deploy proprietary solutions. | ServeAgent | 8 |
| Senior Lead AI Engineer (LLM Gateway, FM Hosting) Senior Lead AI Engineer role focused on building and optimizing LLM inference infrastructure (Gateway, FM Hosting) and related AI components like similarity search, guardrails, evaluation, and observability for enterprise-scale AI products at Capital One. The role involves designing, developing, testing, deploying, and supporting these AI software components, with a strong emphasis on improving performance (scalability, cost, latency, throughput) of large-scale production AI systems. | ServeAgent | 8 |
| Lead AI Engineer (FM Hosting, LLM Inference) Lead AI Engineer focused on optimizing LLM inference for scalable, cost-effective production AI systems within an enterprise setting. The role involves designing, developing, and deploying AI software components, including foundation model training, inference, similarity search, guardrails, evaluation, and observability, leveraging various AI technologies and cloud platforms. | ServeAgent | 8 |
| Sr. Lead AI Engineer (GenAI Platform) Sr. Lead AI Engineer focused on building and scaling GenAI platforms, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. The role involves optimizing performance, cost, and latency of large-scale production AI systems using various open-source and cloud technologies. | ServeAgent | 8 |
| Senior Distinguished Engineer, AI Compute (Remote Eligible) Senior Distinguished Engineer focused on architecting and building the AI compute infrastructure for Capital One's enterprise machine learning platform. This role involves developing scalable, high-performance systems for diverse AI workloads including LLM pre-training, fine-tuning, inference, and agentic applications, leveraging distributed compute frameworks like Ray and Spark on cloud substrates. | ServePretrain | 8 |
| Senior Lead AI Engineer (Gen AI Platform Services, Agentic AI) Senior Lead AI Engineer role focused on building and deploying AI-powered products and foundational AI systems, including LLM inference, agentic AI, similarity search, guardrails, and model evaluation. The role involves optimizing performance, scalability, cost, and latency of large-scale production AI systems. | AgentServe | 8 |