JPMorgan Chase

Lead the development and implementation of GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes within JPMorgan Chase's Private Bank. This role involves designing, deploying, and managing prompt-based models on LLMs, conducting research on prompt engineering, and building data pipelines and evaluation frameworks for these models on cloud platforms.

Vice President Data Scientist role in JPMorgan's Asset & Wealth Management Risk Analytics Group, focusing on developing and deploying advanced AI/ML and LLM solutions for risk management. Responsibilities include identifying use cases, leading model development (pre-training, fine-tuning, optimization), prompt engineering, quantization, evaluation, and collaborating on model serving systems. Requires strong Python, SQL, R, PyTorch/TensorFlow, AWS, and NLP/LLM experience, with frameworks like LangChain/LangGraph/AutoGen.

JPMorgan Chase's Commercial and Investment Bank Chief Analytics Office is seeking an Embedded AI Senior Associate to accelerate high-impact AI programs, focusing on generative AI, LLMs, and Agentic AI. The role involves close collaboration with business experts and analytics/technology teams for end-to-end delivery of AI solutions, ensuring responsible AI use and staying updated with cutting-edge research. Responsibilities include identifying AI solutions, analyzing data, communicating insights to senior stakeholders, and delivering AI solutions.

The role focuses on developing and deploying modular components for agentic systems, enabling intelligent automation and orchestration within business platforms. It involves integrating these capabilities into existing systems, containerizing and deploying them on cloud platforms, and applying advancements in agentic frameworks, LLMs, and GenAI. The role also includes leading a team of senior resources focused on agentic solutions and infrastructure development.

Develops and maintains modular components for agentic systems, integrating AI/ML initiatives, containerizing and deploying on cloud platforms, and leveraging agentic frameworks, LLMs, and GenAI. Requires hands-on experience with agentic systems in production and strong Python skills.

Lead Engineer role focused on designing, building, and productionizing autonomous and assistive AI agents and multi-agent systems. This involves implementing RAG, integrating tools, prompt engineering, guardrails, and orchestrating workflows, with a strong emphasis on scaling and reliability within a fintech domain.

Senior Associate Applied AI Engineer focused on building and deploying agentic AI systems, including multi-agent architectures, RAG pipelines, and tool integration, within a financial services context. The role involves productionizing autonomous agents, orchestrating complex workflows, and ensuring safety through prompt engineering and guardrails.

Associate Applied AI & ML Scientist role at JPMorgan Chase focused on developing and deploying AI/ML solutions for Market Operations. Responsibilities include building ML models, deploying solutions for automation and anomaly detection, architecting generative AI applications, and maintaining production-grade tools. Requires experience with Python, ML libraries, MLOps, LLMOps, and cloud platforms.

Lead Machine Learning Engineer (MLOps) focused on building and maintaining pipelines for distributed model training, model serving, hyperparameter tuning, monitoring, and production validation for a Recommendation Engine team. The role involves deploying ML models on AWS, supporting personalized experiences across consumer channels, and optimizing inference for LLMs and vector databases.

AI Engineer role at JPMorgan Chase focusing on the research, deployment, and optimization of large models, including training, fine-tuning, and prompt engineering. The role involves implementing AI tools in enterprise business scenarios, developing AI platforms, and building AI workflows and agents. Requires strong algorithm and engineering capabilities with experience in RAG, Agent, LangChain, and large model training frameworks.

Lead role focused on designing, deploying, and managing prompt-based LLM models for NLP tasks in financial services. Involves research into prompt engineering, LLM orchestration, agentic AI, building data pipelines, and developing tools for model training, evaluation, and optimization. Requires strong Python, PyTorch/TensorFlow, cloud platform, and MLOps experience.

Director role focused on applying sophisticated machine learning methods (NLP, speech, time series, RL, recommendation systems) to real-world problems within a financial services context. The role involves research, model development, collaboration with business and technology partners, and deploying solutions into production. Emphasis on deep learning expertise, analytical thinking, and driving firm-wide ML initiatives.

This role focuses on applying GenAI, ML, and statistical models, with a strong emphasis on causal inference, to solve business problems in the Global Wealth Management space. The goal is to build next-generation decision systems for pricing, campaign targeting, and understanding cause-and-effect relationships in observational data.

Software Engineer III role focused on designing and delivering AI-powered solutions on SAP BTP, building agentic AI capabilities, and leading proof-of-concept initiatives. Requires experience in AI/ML solution development, SAP BTP, Python/JavaScript, and MLOps practices.

Senior Associate role focused on applying AI/ML techniques, particularly NLP, to solve business problems within the Asset & Wealth Management division of JPMorgan Chase. Responsibilities include developing, productionizing, and documenting AI/ML solutions, collaborating with stakeholders, and coaching team members. Requires 3+ years of applied experience in data science and ML, with strong Python skills and experience with ML libraries and big data tools.

Lead Data Scientist to monitor AI-relevant trends and develop local economic intelligence using LLM models and GenAI tools. Responsibilities include data curation, analytics, modeling, insight generation, and building data pipelines, with a focus on AI disruption monitoring and local economic impact.

Senior Lead Architect role focused on AI strategy, governance, and architecture for AI/ML solutions in financial services, including agentic systems, RAG, and hybrid LLM/SLM models.

Lead Software Engineer focused on building and deploying enterprise-grade AI solutions, including RAG pipelines and agentic AI systems, with a strong emphasis on production-level concerns like scalability, cost, latency, privacy, hallucination mitigation, and observability. The role requires fluency with AI-assisted development tools and hands-on experience in building AI/ML features in production environments.

Quantitative trading role focused on building systematic trading strategies using AI/ML techniques for pricing, risk management, and execution in Rates markets. Involves analyzing large datasets, back testing strategies, expanding modeling tools, and maintaining trading software systems.

Lead Applied AI/ML role at JPMorgan Chase, focusing on developing and productionizing AI/ML solutions for business needs within the HR Data and Analytics team. Requires strong Python, ML techniques, and NLP experience.

Software Engineer III on the Transformative AI Team at JPMorgan Chase, focusing on building an Agentic Private Bank. The role involves developing and implementing GenAI and Agentic AI solutions, working with prompt-based LLMs, and building data pipelines for prompt engineering on cloud platforms.

Lead Software Engineer for Data Engineering role focused on building an AI/Agentic AI layer for a Data Products platform. This involves architecting and developing autonomous agents for data product lifecycle management, leveraging NLP, RAG, Vector Databases, and multi-agent orchestration. The role also requires hands-on technical leadership, mentoring a team, and ensuring scalability, security, and best practices for enterprise-grade data platforms.

Senior Associate role focused on applied AI and quantitative modeling within the Consumer Business Modeling team at JPMorgan Chase. The role involves contributing to the full modeling lifecycle, from defining objectives to developing and communicating results using advanced analytics on large-scale consumer data. Requires an advanced degree in a quantitative field and experience in developing machine learning, optimization, or statistical models.

Lead the development and deployment of AI/ML models for financial services, focusing on retail, credit card, home, auto lending, and wealth management. This role involves end-to-end project leadership, stakeholder management, and ensuring regulatory compliance.

Quantitative trading and research role focused on RMBS and structured products, developing and maintaining models. The role involves applying machine learning and generative AI to credit modeling, data processing, model calibration, performance monitoring, and delivering analytics. Responsibilities include developing financial models, conducting back-testing, performing ML analysis on large datasets, building data analysis platforms, and collaborating with stakeholders.

Lead a team of data analysts to support data annotation and labeling for machine learning models in a fintech domain. This involves developing strategies for training data optimization, identifying patterns in conversational data using NLP, and evaluating the quality of ML outputs. Requires experience in ML data annotation, data modeling, and Python.

Director of Software Engineering leading a team to develop and deploy phygital AI solutions leveraging IoT and edge-to-cloud computing. Focus on integrating AI/ML with IoT ecosystems for real-time analytics and scalable solutions.

Lead the end-to-end lifecycle of AI and ML models for trade surveillance, driving strategic enhancements and innovation using advanced analytics, machine learning, and LLMs. This role involves model development, deployment, monitoring, recalibration, and leveraging GenAI/LLM tools to optimize surveillance processes and operational efficiencies within a regulated financial environment.

This role focuses on applying AI/ML and quantitative techniques to options pricing, hedging, and quoting within financial markets. It involves developing and implementing sophisticated algorithms, quantitative models, and analytical tools for trading analysis and backtesting. The position requires experience with various machine learning techniques and programming languages like Python, KDB, C++, PyTorch, and Sklearn, with a strong emphasis on quantitative research and model development in a regulated financial environment.

Lead Software Engineer in Payment Technology team at JPMorgan Chase, responsible for enhancing, building, and delivering technology products. The role involves driving business impact through technical expertise, problem-solving, and developing secure, high-quality production code. A key focus is on designing, developing, and integrating agent-based systems and LLMs within Java applications, requiring practical cloud-native experience and knowledge of the financial services industry.

Lead Software Engineer at JPMorgan Chase within the consumer and community banking technology team, responsible for driving the architecture, design, and delivery of enterprise-scale platforms and solutions. This includes leading engineering teams, shaping technical strategy, and championing innovation across full-stack development, cloud-native ecosystems, and emerging technologies including Generative AI. The role involves designing and implementing Agentic AI and Generative AI solutions, integrating LLMs, AI agents, and intelligent automation into enterprise platforms, and collaborating with AI/ML teams to bring these capabilities to production at scale.

Applied AI/ML Lead at JPMorgan Chase in Asset & Wealth Management, focusing on developing and productionizing end-to-end AI/ML solutions for business needs. Requires strong Python, ML techniques, and NLP experience.

This role focuses on modernizing Business Intelligence (BI) by integrating AI, specifically LLMs, to create AI-driven BI as a core enterprise capability. The goal is to move beyond descriptive and predictive analytics towards prescriptive insights, operationalizing conversational analytics and building AI assistants within BI workflows. The role involves partnering with senior leadership, owning the BI lifecycle, architecting semantic layers, and driving adoption of AI-enabled BI solutions.

Lead Software Engineer focused on Cloud DevOps and AI, responsible for designing and implementing CI/CD pipelines, infrastructure-as-code, and container orchestration, with a strong emphasis on integrating AI/ML for automation, intelligent monitoring, and agent-based workflow optimization within cloud environments.

Lead Software Engineer on the AI/ML & Data Platforms team, focusing on building AI agents to enhance the software development lifecycle, designing secure Data Lake solutions in AWS, and optimizing deployment processes with Terraform and Python.

AI/ML Engineer role focused on designing, implementing, and deploying AI/ML services for Wholesale Payments Operations. The role involves applying AI/ML techniques for NLP and document understanding, prompt engineering, and designing agentic systems, with a strong emphasis on production-grade solutions and cloud infrastructure.

Executive Director role focused on AI Governance and Model Risk Management within JPMorgan Chase's Risk Management and Compliance division. The role involves designing and implementing AI governance frameworks, assessing AI/ML practices, collaborating with stakeholders, and communicating emerging risks to senior management and regulators. Requires extensive experience in risk management and a strong understanding of GenAI tools.

Senior Director role leading the application of AI, specifically agentic and automation-driven solutions, to transform the management and operation of Mainframe and Midrange Compute platforms within a financial services company. The role involves defining AI strategy, building and scaling an engineering team, implementing agentic AI solutions for infrastructure automation, and ensuring alignment with risk, security, and regulatory requirements.

Executive Director Data Scientist at JPMorgan Chase leading a team focused on AI, machine learning, and advanced analytics for the Card business. The role involves defining AI strategy, delivering AI/ML solutions from ideation to production, supporting customer experience, driving experimentation, and leading a team of analytics professionals. Requires strong leadership, technical depth, and business acumen, with a focus on driving measurable business impact and competitive advantage.

Quant Modeling Associate role focused on ensuring fair lending compliance for various models, including traditional, ML/AI, and Gen AI models, within JPMorgan Chase's Risk Management and Compliance division. Responsibilities include statistical analysis, bias testing, developing mitigation algorithms, research, and documentation, with a focus on adhering to Fair Lending Laws and regulations.

Senior Lead Site Reliability Engineer role focused on building and optimizing AI/ML platforms and products, including infrastructure, serving, and agentic AI solutions for SRE functions. Requires deep SRE experience applied to AI/ML workloads and LLM inference.

This role involves translating business objectives into AI-powered technical solutions for investment research. The associate will own the full solution lifecycle, from requirements gathering to shipping production services and iterating on them. Key responsibilities include building systems that surface insights from various data sources using LLMs, Generative AI, agentic systems, and AWS. The role emphasizes practical business impact and collaboration with business and technology partners.

Senior Lead Site Reliability Engineer role focused on building AI-powered automation systems, intelligent monitoring, and next-generation reliability platforms. The role involves designing and building AI Agents for autonomous operations, integrating various data stores including vector databases, and developing automation scripts. Requires expertise in SRE principles, observability, and AI frameworks like LangChain.

Software Engineer II on the Machine Learning Operations team at JPMorgan Chase, focusing on building and maintaining end-to-end ML solutions for customer-facing experiences. The role involves engineering scalable features and pipelines for relevance and ranking models, implementing ML Ops practices, and ensuring system reliability and observability in a production environment.

Lead Infrastructure Engineer (GenAI) at JPMorgan Chase, focusing on leveraging GenAI and agentic AI for cloud infrastructure cost optimization. The role involves developing, implementing, and evangelizing AI-driven solutions and intelligent agents to automate cost management and improve efficiency across on-premises and cloud environments. Responsibilities include leading the development of GenAI agents for cost optimization, discovering opportunities using AI models, architecting automation runbooks, and guiding stakeholders on cost-effective decisions.

Software Engineer III with Python, AWS, and GenAI experience at JPMorgan Chase in Bengaluru. This role involves partnering on LLM projects, collaborating on ML products, and developing scalable ML-driven products within a regulated environment. Requires strong Python, cloud (AWS/Azure), and SDLC experience.

Software Engineer III - AI Reliability Engineer at JPMorgan Chase within Asset and Wealth Management Technology team, focused on enhancing the reliability and resilience of AI systems, particularly large language model serving and training systems. Responsibilities include developing SLOs for AI systems, implementing monitoring, designing high-availability serving infrastructure, championing site reliability culture, developing automated failover and recovery systems, creating AI Incident Response playbooks, leading incident response for critical AI services, building cost optimization systems, engineering for scale and security, and collaborating with ML engineers. Requires formal training/certification in software engineering, proficiency in reliability best practices, observability tools, CI/CD, container orchestration, and understanding AI infrastructure challenges. Preferred qualifications include experience with AI-specific observability tools, AI incident response strategies, AI-centric SLOs/SLAs, and continuous evaluation processes.

Senior Lead Software Engineer role focused on building and optimizing AI/ML infrastructure platforms within a financial services company. The role involves architecting cloud infrastructure, managing ML workloads, CI/CD pipelines, and collaborating with AI teams to meet computational needs. Requires strong software engineering, Kubernetes, cloud, and foundational ML knowledge.