JPMorgan Chase

Lead the design and delivery of LLM-powered solutions for commercial banking use cases like content extraction, enterprise search, reasoning, summarization, and recommendations. Partner with engineering and product teams to deploy reliable, scalable, and governed GenAI capabilities using Amazon Bedrock and Cortex, with a strong emphasis on evaluation, guardrails, and production-grade MLOps.

Sr. Manager of Software Engineering for an AI/ML Solutions technology team focused on building and enhancing AI/ML platforms, Gen AI use cases, LLM fine-tuning, and multi-agent orchestration. The role involves hands-on code development, team management, and adopting best practices in software engineering and MLOps.

This role focuses on building and deploying AI/ML/LLM/GenAI solutions, with a strong emphasis on fine-tuning generative models for specific use cases, building AI agents, and implementing RAG-based solutions. The role also involves developing data pipelines, evaluating models, and communicating results.

Senior Lead Architect role focused on designing and developing an AI/ML platform, managing engineering teams, and driving the adoption of AI/ML solutions within financial services. The role involves overseeing the end-to-end lifecycle of AI/ML projects, ensuring scalability, performance, and compliance.

Lead data labeling initiatives for financial industry ML models, focusing on creating reliable datasets, managing annotation operations, and driving innovation in data quality and evaluation, including LLM-as-judge and agentic workflows.

Lead a team of software engineers to architect and develop an enterprise-grade Agentic AI platform, focusing on multi-agent orchestration, tool integration, memory, RAG, guardrails, observability, and evaluation. This role requires strong technical leadership in AI agents and people management skills to drive strategic vision and hands-on work.

Senior Applied AI/ML Engineer to build and operate LLM-driven applications and ML products in a regulated fintech environment. Focus on agentic frameworks, prompt optimization, and fine-tuning LLMs, with a secondary focus on post-training techniques.

Senior Associate Data Scientist focused on applied AI/ML, specifically GenAI and LLMs, for fintech. Responsibilities include designing, developing, and deploying AI systems, integrating GenAI into platforms, optimizing performance, and ensuring responsible AI governance and compliance. Requires strong Python, ML framework, and MLOps experience, with a focus on agentic workflows and production deployment.

Senior Associate Data Scientist role in JPMorgan's AWM Risk Analytics Group, focusing on developing and deploying AI/ML models for risk management. Responsibilities include identifying use cases, leading model development (NLP, LLMs), prompt engineering, quantization, evaluation, data onboarding, model optimization, and contributing to risk methodologies. Requires experience with Python, SQL, R, PyTorch/TensorFlow, AWS, and frameworks like LangChain.

Director level role focused on engineering and scaling GenAI capabilities, specifically agentic workflows and RAG systems, within an enterprise setting. The role involves building reusable frameworks, shared components, and establishing best practices for production-grade AI solutions, with a strong emphasis on evaluation, monitoring, and governance. The position requires deep software and data engineering expertise, cloud-native experience, and the ability to translate complex technical concepts to senior stakeholders.

Associate Senior Data Scientist at JPMorgan Chase in Bengaluru, focusing on designing, deploying, and managing prompt-based LLM models for NLP tasks in financial services. The role involves prompt engineering, LLM orchestration, building data pipelines, and developing tools for model training and evaluation, with a requirement for Python, PyTorch/TensorFlow, cloud platforms, and MLOps.

Lead Applied AI ML role in Fintech, focusing on developing and productionizing ML models and scalable data pipelines for personalized financial services. Requires strong CS background, end-to-end project ownership, and collaboration with cross-functional teams. Emphasis on research, algorithm implementation, experimentation, and ensuring regulatory compliance.

JPMorgan Chase is seeking a Vice President for their Applied Artificial Intelligence & Machine Learning team in Mumbai. This role focuses on developing and deploying scalable ML solutions for KYC and operations, with potential for commercialization. The position requires a Master's or PhD in a quantitative field, strong Python skills, and experience deploying ML models in production at scale. Experience with NLP, LLMs, and Agentic AI systems is preferred.

This role focuses on designing, developing, and deploying advanced AI/ML models for investment risk and analytics within a fintech domain. It involves the full lifecycle of AI/ML initiatives, from data engineering and model development to deployment and monitoring, with an emphasis on generative AI, deep learning, and reinforcement learning. The role also requires risk modeling, scenario analysis, and collaboration with technology and business stakeholders, including participation in governance and regulatory forums.

JPMorgan Chase is seeking a Quant Modeling Associate with 3+ years of experience in AI/ML to design, develop, and deploy advanced AI/ML models for investment risk oversight. The role involves the full lifecycle of AI/ML initiatives, including data engineering, model development, validation, deployment, and monitoring, with a focus on generative AI and deep learning techniques. The associate will collaborate with technology teams, present findings, and stay updated on AI trends, while also participating in governance and regulatory discussions.

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 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.

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 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.

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.

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.

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.

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.

This role focuses on designing and executing data-driven control testing within financial services, specifically for client onboarding and documentation. It involves building and productionizing AI/ML solutions, including Generative AI, for risk detection, continuous monitoring, and regulatory compliance. The role also emphasizes automating testing processes, operationalizing alerts, and partnering with various teams to ensure explainability and auditability, with a strong focus on regulated environments.

Develops and implements LLM-based solutions, including RAG agents and agentic AI systems, with a focus on data analysis, model tuning, and evaluation metrics within a fintech domain.

Software Engineer III role focused on building agentic AI solutions within a large enterprise environment. Requires proficiency in Python/Java, system design, cloud technologies (AWS), and containerization. Familiarity with agent frameworks (LangGraph, Autogen), LLM evaluation tools, and model training/inference technologies is preferred.

Lead Software Engineer focused on building agentic AI systems, including multi-agent orchestration, tool-use chains, planning/reasoning loops, and memory architectures. Requires strong foundation in distributed systems, API design, microservices, event-driven architectures, and cloud platforms. Experience with LLMs, prompt engineering, fine-tuning, and evaluation is preferred.

This role focuses on applying AI and data science, particularly LLMs, to enhance research methodologies and automate workflows within JPMorgan Chase's Global Research Center. The specialist will collaborate with analysts to identify opportunities for data-driven insights and workflow automation, ensuring data integrity and adhering to regulatory standards.

Software Engineer III role focused on designing, developing, and maintaining backend services for AI and Agentic AI applications using Python. The role involves building and integrating agentic frameworks (Langgraph, Langchain, Autogen, Google ADK) for autonomous marketing solutions, developing APIs for AI-driven workflows, and deploying applications on AWS.

Lead the design and delivery of AI/ML solutions for Market Operations at JPMorgan Chase. This role involves managing a team, partnering with business and technology stakeholders, and ensuring the successful implementation and scaling of AI-driven tools. The focus is on developing and deploying production-grade AI/ML applications, establishing best practices for evaluation and monitoring, and driving measurable impact.

Seeking an AI engineer to develop and productionize NLP models and services for bank operations, focusing on end-to-end projects and scalable data pipelines.

JPMorgan Chase is seeking a Data Scientist Associate for their APAC Payments Data & Analytics team. The role focuses on delivering high-impact AI & Analytics use cases, driving data strategy, and creating data-driven products for clients and internal partners. Responsibilities include exploring money movement, forging partnerships with stakeholders, identifying and integrating Agentic AI workflows, and collaborating with product managers. Requires 4+ years of experience in Data Science, Analytic Solutions, and GenAI/Agentic AI use cases, with expertise in data platforms (Databricks, PySpark, Tableau) and cloud platforms (AWS). Familiarity with LLMs, prompt engineering, and RAG/MCP integrations is essential. Experience in payments or consulting is preferred.

Senior Manager of Software Engineering for Machine Learning & Cloud within the Corporate Technology - Consumer & Community Bank Risk group at JPMorgan Chase. The role involves leading a team of 10, mentoring technical teams, and driving the development and productionization of ML models using cloud services and MLOps practices. Responsibilities include technical leadership, code development, troubleshooting, and managing ML pipelines.

This role is for an Applied AI/ML Engineer (VP level) at JPMorgan Chase, focusing on developing and delivering AI solutions for Commercial & Investment Banking Payments. The engineer will collaborate with cross-functional teams to boost operational efficiency and drive business innovation using AI/ML technologies, managing the code lifecycle from development to production.

Applied AIML Engineer at JPMorgan Chase, focused on developing and delivering AI-driven payments solutions for Commercial & Investment Banking. The role involves coding, cross-functional collaboration, and end-to-end code lifecycle management, with a minimum of 2 years of AIML project delivery experience.

Develops and industrializes quantitative methodologies and predictive models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.

Develops quantitative methodologies and predictive/explanatory models using statistical and machine learning techniques in R/Python for financial planning and forecasting within a large financial institution. The role involves data analysis, model development, documentation, and interaction with model governance.