Lead Software Engineer - Cloud/AI Engineer

JPMorgan Chase & Co.; Plano (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer in Plano, TX, to advance enterprise AI-enabled products within the Corporate Sector—Data Visualization & BI. You will be a core technical contributor, driving secure, scalable solutions across multiple business functions.

The role emphasizes an AI-forward mindset, hands-on building of AI/ML features, and leadership in agile teams. Expect advanced engineering challenges with state-of-the-art tools and architectures.

Qualifications

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Fluency with AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Cursor) used daily.
• Hands-on experience building AI/ML-powered features or products (RAG, AI agents, LLMs).
• 3+ years of AWS cloud services expertise.
• Proficiency in Python and Django or another web backend framework.
• Experience with React or similar UI frameworks.
• Terraform and infrastructure-as-code experience.
• Strong systems design, data structures, and algorithms knowledge.
• Ability to operate in fast-changing environments and deliver quickly.
• Structured, evidence-based decision-making.

Responsibilities

• Leverage AI-powered coding assistants as core tools in daily development workflows—writing, reviewing, debugging, and refactoring code with speed and precision.
• Validate and iterate on AI-generated outputs using sound engineering judgment.
• Evaluate and drive adoption of new AI tools for productivity and quality gains.
• Design, build, and deploy enterprise-grade AI solutions including RAG pipelines and LLM-powered workflows.
• Architect AI systems with production concerns: scalability, cost, latency, privacy, and observability.
• Develop and deploy agentic AI solutions with identity and guardrails in place.
• Write secure, high-quality production code and review others' code.
• Understand end-to-end system interactions and how changes propagate across distributed systems.

Skills

AI-assisted development

AI/ML features

Python

React

Django

AWS

Terraform

System design

Problem-solving

Delivery under pressure

Education

Formal training in software engineering

Tools

GitHub Copilot

Claude

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 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