Applied AI/ML Lead

About the role

Be an integral part of an innovative and forward-thinking agile team to enhance, build, and deliver advanced technology products. As an Applied AI/ML Lead within the Corporate Sector, Trade Surveillance Technology team, you will be responsible for delivering AI/ML enabled analytical and technical solutions that support market surveillance and regulatory compliance capabilities. You will work across the full model lifecycle — from problem framing and data exploration to model development, productionization, and monitoring — partnering closely with data science and engineering teams. You will translate ambiguous business needs into robust, production-grade AI systems while championing sound engineering and responsible AI practices. This is a hands-on technical role with growing scope for technical leadership, mentorship of junior engineers, and influence over architectural decisions.

Responsibilities

• Model development: Design, train, evaluate, and fine-tune machine learning, deep learning, and large language models (LLMs) to address defined business use cases.
• Productionization (MLOps): Build and maintain scalable, reliable ML/Feature/Data pipelines for training, deployment, inference, and monitoring in production environments; collaborate with data engineering to work with large, complex datasets — performing feature engineering, data validation, and preprocessing to ensure model quality and reproducibility.
• Applied research: Stay current with advances in AI/ML (e.g., generative AI, RAG, agentic frameworks) and prototype new techniques to evaluate their applicability.
• System integration: Integrate models and AI services into applications via APIs, ensuring performance, latency, and cost efficiency.
• Quality and governance: Implement testing, evaluation frameworks, and monitoring to detect drift, bias, and degradation; adhere to responsible AI and model risk standards.
• Cross-functional partnerships: Collaborate with data scientists and software engineers to scope requirements and deliver end-to-end solutions.
• Documentation and mentorship: Produce clear technical documentation and provide guidance and code review support to junior team members.
• Project leadership: Lead projects end to end — from understanding and establishing requirements, to design and final implementation/testing/delivery.

Requirements

• Master's degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience).
• 6+ years of hands-on experience building and deploying ML/AI models in production settings.
• Strong programming proficiency in Python, SQL, relational databases, and Linux.
• Familiarity with AI/ML frameworks such as scikit-learn, PyTorch, SmartSDK for agent building, and base libraries such as pandas, NumPy.
• Solid understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, and optimization.
• Experience with generative AI / LLMs, including prompt engineering, fine-tuning, embeddings, and retrieval-augmented generation (RAG).
• Proficiency in MLOps tooling and practices: containerization (Docker), orchestration (Kubernetes), CI/CD, model versioning, and pipeline tools (e.g., MLflow, Kubeflow, Airflow).
• Experience with internal cloud environments such as Gaia and GKP, and creating pipelines to deploy AI/ML models onto these platforms.
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and its ML services.
• Strong data skills: SQL, data pipelines.
• Familiarity with software engineering best practices: version control (Git), testing, code review, and API design.
• Experience with GenAI Gateway services integration for LLM-based solutions and Phoenix integrations for telemetry/evaluation/cost analysis.

Preferred qualifications

• Experience with vector databases, agentic AI frameworks, or LLM evaluation methodologies.
• Exposure to responsible AI, model risk management, or regulated-industry AI deployment.
• Working with distributed data processing (e.g., Spark) is a plus.

Benefits

JPMorganChase offers a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. Benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching, and more. Additional details about total compensation and benefits will be provided during the hiring process.