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Applied AI ML Lead
lead · Artificial Intelligence
Shape the future of software delivery by building applied artificial intelligence solutions that are secure, reliable, and measurable. Join a team that turns advanced methods into production capabilities used by real customers, with strong engineering standards and clear ownership. Bring your expertise in building distributed systems and grow your impact through high-visibility work, deep technical leadership, and cross-team collaboration
As an Applied AI ML Lead at JPMorgan Chase, you will architect, build, and operate trusted applied artificial intelligence capabilities used in production. You will partner with product and engineering teams to deliver workflow automation and retrieval-based solutions that improve user outcomes. You will translate technical tradeoffs across quality, latency, cost, and controls into clear recommendations that accelerate delivery and measurable business impact.
In this role, you will focus on production engineering for applied artificial intelligence, including retrieval-based question answering and tool-enabled workflow automation. You will set engineering patterns that improve consistency and reuse across teams, while maintaining strong standards for security, access controls, and operational resilience. You will define how solutions are evaluated, monitored, and improved over time based on real usage and measurable outcomes. This is a hands-on role with significant influence through design leadership, reviews, and shared practices.
Job Responsibilities
- Own end-to-end technical delivery for applied artificial intelligence and machine learning solutions, from requirements shaping through production support.
- Architect retrieval‑augmented generation solutions, including data ingestion, content segmentation, embedding, indexing, retrieval, re-ranking, and relevance tuning.
- Implement tool-enabled, agent-based workflows with orchestration, state management, retries, permissions, guardrails, and observability.
- Define evaluation strategies, including offline test suites, curated datasets, human review where needed, continuous evaluation, and telemetry-driven monitoring.
- Build reusable engineering patterns and reference implementations that reduce duplication and speed delivery across teams.
- Drive reliability, performance, and cost outcomes through benchmarking, capacity planning, latency optimization, and post-incident learning.
- Embed security and governance by design, including sensitive-data-aware handling, access controls, auditability, and disciplined release practices for models and prompts.
- Influence technical direction through clear design documents, design reviews, and pragmatic decision-making aligned to partner needs and control requirements.
- Coach peers through pairing, feedback, and best-practice guidance without direct people management.
- Champion a culture of opportunity, inclusion, and respect in day-to-day collaboration.
Required Qualifications, Capabilities, and Skills
- 8+ years of hands-on software engineering and/or data engineering experience delivering production distributed systems.
- Proficiency coding in Python and/or Java, including building, testing, and operating services in production.
- Demonstrated delivery of applied artificial intelligence and/or machine learning solutions to production, including monitoring and iterative improvement based on usage.
- Expertise designing retrieval‑augmented generation architectures, including embedding-based search, retrieval/indexing, re-ranking, grounding methods, and quality evaluation.
- Experience building tool-enabled workflow automation (agent-based patterns) with orchestration, state management, retries, and safety guardrails.
- Strong data engineering fundamentals, including data quality controls, idempotent pipelines, backfills, and metadata/lineage concepts.
- Experience deploying and operating secure, cloud-native services on Amazon Web Services, including performance, latency, and cost management.
- Strong communication skills to explain technical tradeoffs and recommendations to product and engineering stakeholders.
Preferred Qualifications, Capabilities, and Skills
- Bachelor’s or Master’s degree in Computer Science or equivalent practical experience.
- Working knowledge of PyTorch or TensorFlow to partner effectively on training, fine-tuning, and evaluation.
- Experience automating evaluation and quality gates for applied artificial intelligence systems, including regression testing, continuous evaluation, and versioning for prompts and models.
- Familiarity with responsible artificial intelligence practices appropriate for regulated environments, including documentation, testing, and control-aligned design.