Director of Software Engineering Python, Gen AI

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 toship real software that solves real problems. You are someone who isendlessly 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.

Required qualifications capabilities and skills

• Formal training or certification on Machine Learning concepts and 10 years applied experience in programming languages like 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 themodern GenAI stack: LLM APIs (OpenAI Anthropic etc.) RAG architectures prompt engineering vector databases embeddings tokenization and evaluation of generative outputs.

• Familiarity withAI-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 dont wait to be told what to learn.

• High energy and bias toward action You move fast iterate and ship; youre 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.

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.