Join our innovative team and shape the future of software development with AI-driven solutions.
As an Director of software Engineering at JPMorgan Chase within Asset and Wealth Management, you will work closely with financial advisors, client service, product, operations, and risk and control partners — not just to prototype ideas, but to ship real software that solves real problems. You are someone who is endlessly curious, energetic, and driven to build — someone who sees AI not as an academic exercise but as a practical superpower to be wielded through great engineering. Your expertise in modern AI tools and techniques — particularly the GenAI ecosystem will be leveraged to consistently challenge the norm, innovate for business impact, and spearhead the strategic development of new and existing products and technology portfolios. You thrive on ambiguity, love learning new things fast, and have the energy to push ideas from napkin sketch to production. You are comfortable using AI-assisted development tools (e.g., Claude Code, GitHub Copilot, Cursor) as part of your daily workflow and are excited about what these tools mean for the future of software engineering.
Job Responsibilities
- Builds and ships production of AI solutions — Design, develop, test, and deploy AI-powered applications and services end-to-end, with a focus on reliability, maintainability, and clean software engineering practices.
- Partners with the business to define the right problems — Collaborate with stakeholders to translate ambiguous business needs into well-scoped technical approaches with clearly measurable success criteria.
- Join our innovative team and shape the future of software development with AI-driven solutions.— Implement retrieval-augmented generation (RAG) pipelines, prompt engineering strategies, agentic workflows, evaluation frameworks, and guardrails for LLM-based systems.
- Leverages AI-assisted development tools — Use Gen3 AI coding tools (Claude Code, GitHub Copilot, Cursor, etc.) as force multipliers in your daily development workflow; contribute to team best practices for AI-augmented engineering.
- Communicates clearly and build trust — Present results, system behavior, trade-offs, and business impact to both technical and non-technical audiences with clarity and confidence.
- Documents rigorously — Maintain clear documentation of system design, experiments, and decision rationale, including model risk artifacts, validation evidence, and reproducibility details.
- Builds reusable tooling and infrastructure — Contribute to shared libraries, evaluation harnesses, prompt libraries, and pipelines that scale AI capabilities across multiple use cases.
- Collaborates across the firm — Work with other JPMorganChase AI/ML teams and partner with legal, compliance, privacy, cybersecurity, and model risk to deliver safe, responsible, and compliant solutions.
- Contributes to operational excellence — Support MLOps and LLMOps practices for deployment, monitoring, continuous improvement (drift, performance, cost, fairness), and incident response.
- Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on Machine Learning concepts and 10+ years applied experience in programming languages like Python. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Strong software engineering skills in Python; comfort with software fundamentals including testing, version control (Git), code review, CI/CD, and writing clean, maintainable, production-quality code.
- Experience with modern development practices: containerization (Docker), REST APIs, cloud services (AWS or similar), and infrastructure-as-code basics.
- Hands-on experience building and deploying software systems — not just notebooks or prototypes. Practical experience with the modern GenAI stack: LLM APIs (OpenAI, Anthropic, etc.), RAG architectures, prompt engineering, vector databases, embeddings, tokenization, and evaluation of generative outputs.
- Familiarity with AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, or similar) and a point of view on how they change the way software is built.
- Ability to evaluate and iterate on AI system performance using both intrinsic metrics and business-aligned outcomes; comfort designing lightweight evaluations and feedback loops. Awareness of responsible AI principles: bias, fairness, hallucination mitigation, guardrails, and red-teaming for GenAI systems.
- Exceptional problem-solving ability — You can take a vague, messy problem and break it into tractable pieces, then drive to a working solution.** Deep curiosity** — You independently explore new tools, techniques, and research; you don't wait to be told what to learn.
- High energy and bias toward action — You move fast, iterate, and ship; you're not afraid to build a rough version to learn from.** Strong collaboration instincts** — You work effectively with engineers, data scientists, business partners, and control functions; you communicate clearly and build trust.
- Detail-oriented with the ability to manage multiple workstreams and meet production timelines.
- Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs
- Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
Preferred qualifications, capabilities, and skills
- Experience with machine learning fundamentals (classification, regression, clustering, basic NLP) — enough to know when classical ML is the right tool vs. GenAI.
- Familiarity with deep learning frameworks (PyTorch, TensorFlow) and the Hugging Face ecosystem.
- Exposure to big data technologies (Spark, distributed systems) or GPU-accelerated workloads. Background in mathematics and statistics (probability, optimization, experimental design); familiarity with A/B testing or causal evaluation basics.
- Knowledge of financial markets, wealth management products, or advisor/client workflows. Experience with model risk management, validation documentation, and regulatory considerations for AI/ML systems.