Applied AI/ML Lead
JPMorgan Chase · Jersey City
Be an integral part of a 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 the 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.
Job 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 and data engineering collaboration 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 and cross-functional partnerships to ollaborate 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 and lead projects end to end to guide and lead projects from understanding and establishing requirements, to design and final implementation/testing/delivery
Required Qualifications, Capabilities, and Skills
- Master's degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, or a related quantitative field (or equivalent practical experience) and 6+ years of hands-on experience building and deploying ML/AI models in production settings.
- Strong programming proficiency in Python/SQL/Relational Databases/Linux and familiarity with AI/ML frameworks such as scikit-learn, PyTorch, SmartSDK for Agent building, base libraries such as pandas, NumPy, etc.
- Solid understanding of ML fundamentals: supervised/unsupervised learning, deep learning, model evaluation, and optimization with 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) with 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 with strong data skills: SQL, data pipelines
- Familiarity with software engineering best practices: version control (Git), testing, code review, and API design along with experience with GenAI Gateway services integration for LLM based solution and Phoenix integrations for telemetry/eval/cost analysis
Preferred Qualifications, Capabilities, and Skills
- 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.
We offer 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. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These 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.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
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