Lead Software Engineer - Cloud​/AI Engineer

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

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 JPMorgan

Chase 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., Git Hub 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., Git Hub 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 (Lang Chain, Llama Index, 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 Dev Ops practices
• Knowledge of…