Lead Software Engineer - AI

Join our technology team and help drive the future of AI-powered automation and IoT at JPMorganChase. You will lead high-impact initiatives that transform business processes across Workplace, Smart Building, and connected device ecosystems. As a Lead Software Engineer, you’ll build modern full-stack solutions, collaborate with talented engineers and product partners, and shape technical direction in a dynamic environment where innovation and ownership are valued.

As a Lead Software Engineer in our Automation & AI Solutions team, you will architect, design, and deliver scalable software products that combine cloud-native microservices, IoT integrations, and generative AI/LLM capabilities. You will own key technical decisions, drive engineering best practices, and ensure solutions are secure, reliable, and production-ready.

Job responsibilities

• Design and develop creative full-stack software solutions using innovative approaches.
• Lead the creation and implementation of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.
• Build and evolve IoT platform integrations (device telemetry, commands, events, device onboarding/management) for Smart Building and Workplace systems.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Architect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.
• Write secure, high-quality production code; review, mentor, and unblock other engineers through code reviews and technical leadership.
• Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).
• Communicate project status clearly and manage priorities across multiple initiatives.
• Collaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, equity, and inclusion.
• Lead complex projects supporting system design and operational stability, specifically in Workplace, IoT, and Smart Building systems.

Required qualifications, capabilities, and skills

• Formal training or certification in software engineering with 6+ years of professional experience.
• Strong system design, application development, and operational stability skills in production environments.
• Advanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a plus.
• Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).
• Familiarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit-learn, Hugging Face.
• Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).
• Expertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).
• Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices.
• Strong communication skills and a proactive approach to continuous improvement.
• Deep understanding of IoT architectures, protocols, device management patterns, and security best practices.
• Experience integrating IoT solutions within smart building systems / property management platforms, including cloud integration and edge computing patterns.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Track record delivering scalable, reliable, and secure products from concept to launch.

Preferred qualifications, capabilities, and skills

• Cloud certification in AWS, GCP, or Azure.
• Practical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).
• Familiarity with Spark, distributed computing, and platforms such as Databricks.