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 an Applied AI ML Director in London to lead a team of specialists in developing horizontal AI capabilities (APIs, libraries) for Corporate and Investment Banking. The role involves collaborating with Line of Business AI teams, mentoring engineers, and staying at the forefront of AI advancements, with a focus on domains like Documents, Email, and Speech. Requires a PhD or top commercial AI lab experience, hands-on model training/deployment, and experience leading AI teams.

Associate Data Scientist focused on NLP/LLM applications within Asset Management, responsible for developing and deploying agentic frameworks, applying ML techniques to improve decision-making, and collaborating with stakeholders to reengineer processes and enhance client experiences.

Lead engineer for an enterprise-scale generative AI and agentic AI initiative focused on natural language querying of structured and unstructured data. The role involves designing and deploying multi-agent systems, NLQ frameworks, and data pipelines, with a strong emphasis on production code quality and AWS infrastructure.

Quantitative Analyst with a focus on AI/ML/LLMs for investment research, alpha generation, portfolio optimization, and risk management in financial markets. Responsibilities include researching and implementing quantitative models, applying NLP to textual data, identifying alpha signals, and collaborating with teams to implement models in production. Requires a quantitative Master's/PhD, 3+ years of experience, expertise in ML/DL/AI/LLMs/NLP, strong coding skills (Python), and knowledge of financial markets. Regulatory licenses are required.

Senior Associate role focused on building and scaling production LLM agents for finance workflows, involving RAG, tool use, multi-step reasoning, evaluation frameworks, guardrails, and MLOps practices on cloud platforms.

Senior Lead Software Engineer focused on architecting, building, and scaling autonomous AI agent systems. Requires deep experience in agentic AI, LLMs, Python frameworks (LangChain, etc.), cloud platforms, and vector databases. The role involves technical leadership and driving the delivery of production-grade AI agent systems.

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.

AI Researcher Senior Associate at JPMorgan Chase, focused on developing novel AI/ML techniques and models for complex, large-scale problems within the financial services domain. The role involves research projects, formulating problems, developing algorithms, conducting experiments, and contributing to business applications, open-source software, and patents.

This role focuses on applying AI/ML and GenAI solutions within the Home Lending sector of JPMorgan Chase. The Lead will design, develop, and deploy AI/ML/GenAI solutions, including LLMs, RAG, NLP, and AI Agents, and build MLOps/LLMOps pipelines on cloud platforms. The role requires extensive experience in applied AI/ML engineering, production deployment, and proficiency with ML frameworks and cloud technologies.

JPMorgan Chase's Chief Data & Analytics Office is seeking an Executive Director to lead Machine Learning initiatives. This role involves applying advanced ML techniques (NLP, speech, time series, RL, recommendations) to real-world problems, collaborating with cross-functional teams to deploy solutions, and developing large-scale frameworks for ML model application across the business. The position requires a strong background in ML/DL, experimental design, and communication skills, with a focus on production deployment within a regulated financial environment.

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.

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.

JPMorgan Chase is seeking an Applied AI ML Researcher Senior Associate to build next-generation autonomous agents for the financial industry. This role focuses on developing GenAI and Agentic solutions to automate complex operational processes, deliver collaborative agents that orchestrate workflows, and contribute to the production deployment of AI/ML solutions.

Research Scientist role focused on applying sophisticated machine learning methods, particularly NLP and LLMs, to real-world problems within a financial services context. The role involves developing state-of-the-art models, publishing research, and collaborating to deploy solutions into production. It emphasizes both research exploration and practical application, with a focus on post-training and potentially agentic systems.

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 advanced ML methods like time series analysis, reinforcement learning, causal inference, and NLP to solve real-world problems in finance. The associate will research new methods, develop state-of-the-art models, collaborate with various teams to deploy solutions, and drive firm-wide initiatives. A PhD in a quantitative discipline and hands-on experience with ML toolkits are required, with a preference for candidates with financial industry knowledge and published research.

Quantitative Equity Researcher (VP) at JPMorgan Chase focused on developing novel alpha signals and enhancing return-forecasting models using advanced statistical, econometric, and machine learning techniques, including LLMs and generative AI. The role involves applying these techniques to large datasets, designing research pipelines, building research frameworks, and collaborating with portfolio managers and technology teams to integrate research into production systems. Requires strong Python and SQL skills, and experience with NLP and alternative data for repeatable research.

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.

Product Director, Solutions Engineering role focused on driving adoption of an AI platform (Fusion) within a financial services context. The role involves leading a team of engineers, collaborating with clients to co-design AI solutions, developing common solutions, and ensuring AI solutions are delivered to production with a focus on scale, reliability, and controls.

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.

Senior Product Associate role focused on developing and launching AI-enabled internal capabilities and experiences within JPMorgan Chase's Employee Platforms. The role involves identifying product opportunities, running discovery loops, and leveraging AI (LLMs, RAG, agents) as a core toolset across the product lifecycle. Requires a hands-on approach, collaboration with engineering and data science, and a strong understanding of LLM application concepts and AI quality signals.

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.