Lead Software Engineer - Cloud/AI Engineer

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