We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Corporate Sector - Data Visualization & BI team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
This role requires a strong AI-forward mindset. We are looking for engineers who don't just use AI — they think with it, build with it, and know when not to use it.
Job Responsibilities
- Leverage AI-powered coding assistants (e.g., GitHub Copilot, Claude) as core tools in daily development workflows — writing, reviewing, debugging, and refactoring code with speed and precision
- Validate, critique, and iterate on AI-generated outputs rather than accepting them uncritically; apply sound engineering judgment to AI suggestions
- Continuously evaluate emerging AI tools and techniques, driving adoption where they deliver measurable productivity and quality gains
- Design, build, and deploy enterprise-grade AI solutions including Retrieval-Augmented Generation (RAG) pipelines, agentic AI systems, and LLM-powered workflows
- Architect AI systems with production-level concerns: scalability, cost management, latency, data privacy, hallucination mitigation, and observability
- Design, build, and deploy agentic solutions with enterprise grade identity, guardrails, tracing etc.
- Execute creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches. Develop secure, high-quality production code and review and debug code written by others
- Apply strong systems thinking — understand how components connect end-to-end, where failures occur, and how changes propagate across distributed systems
- Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability
- Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability within existing systems
- Influence stakeholders and drive alignment across teams without direct authority, and own outcomes end-to-end — take accountability when things go well and when they don't
Required Qualifications, Capabilities, and Skills
- Formal training or certification in software engineering concepts and 5+ years of applied experience
- Demonstrated fluency with AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Cursor) — not just familiarity, but daily integrated use
- Hands-on experience building AI/ML-powered features or products — RAG systems, AI agents, prompt engineering, or LLM integration in production or near-production environments
- 3+ years of hands-on experience with AWS cloud services
- Proficiency in Python programming
- Experience with Django or another web backend framework
- Experience with React or another modern UI framework
- Strong experience with Terraform and infrastructure-as-code principles
- Solid understanding of system design, data structures, and algorithms
- Demonstrated adaptability — ability to operate effectively in fast-changing, ambiguous environments and deliver at speed
- Strong problem-solving skills with a structured, evidence-based approach to decision-making
Preferred Qualifications, Capabilities, and Skills
- Experience with AI orchestration frameworks (LangChain, LlamaIndex, CrewAI, Google ADK, or similar)
- Experience with vector databases (Pinecone, Weaviate, pgvector, Chroma, or similar) and embedding models
- Understanding of LLM evaluation, guardrails, and responsible AI practices (accuracy, cost, bias, data privacy)
- Exposure to Data Engineering tools and platforms, especially Databricks
- Familiarity with CI/CD pipelines and DevOps practices
- Knowledge of other cloud platforms (Azure, GCP) is a plus