Lead Software Engineer, Cloud Platforms

Lead Software Engineer – Cloud Platform in the Corporate AI and Machine Learning Data Platforms Team

Are you ready to shape the future of technology at a global financial leader? Join us and make a real impact by building advanced cloud platforms that power data-driven decision making. At JPMorgan Chase, you’ll collaborate with top talent, leverage cutting-edge tools, and help deliver solutions that transform how we work. This is your chance to push boundaries, grow your expertise, and be part of a team that values innovation and continuous learning. Discover a career adventure where your ideas matter and your skills drive change.

As a Lead Software Engineer, Cloud Platform at JPMorgan Chase within the Corporate Sector AI and Machine Learning Data Platforms team, you will play a key role in designing and delivering secure, high-quality technology solutions that support our data and analytics strategy. You’ll work with us to develop cloud-based products, enhance productivity, and enable responsible innovation across the firm.

Together, we’ll harness the power of artificial intelligence and machine learning to create new opportunities and drive business success. You’ll have the chance to collaborate, lead, and grow in a dynamic, inclusive environment.

Job Responsibilities

Provide technical leadership and guidance to the cloud engineering team.

Lead the design and development of secure, scalable, and reliable cloud infrastructure and platform tools.

Drive adoption of modern DevEx (Developer Experience) practices and evolve CI/CD and developer tooling to improve delivery speed, quality, and consistency.

Align platform strategy and roadmaps with business priorities; lead cross-functional initiatives to modernize SDLC practices.

Evaluate, integrate, and govern strategic tooling to reduce cognitive load and improve developer experience.

Collaborate with development teams to identify and eliminate bottlenecks on the platform.

Define and promote paved paths and self-service workflows to streamline developer workflows.

Implement real-time telemetry pipelines and workflows for large-scale platform observability and analytics.

Champion adoption of tools that can improve developer productivity through clear documentation, training, office hours, and close engagement with the developer community.

Standardize use of AI-assisted coding tools and AI-powered development ecosystems to accelerate development workflows, code generation, and engineering productivity across the organization.

Contribute to the design and development of AI agents and Model Context Protocol (MCP) integrations using frameworks built on top of Google ADK, Anthropic SDKs, etc. and related tooling to enable intelligent, scalable platform automation.

Required Qualifications, Capabilities, and Skills

Formal training or certification on software engineering concepts and 5+ years applied experience

Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP).

Advanced knowledge of containerization and orchestration platforms (Docker, Kubernetes, ECS, etc.).

Demonstrated expertise in DevEx (Developer Experience) and CI/CD tools (Jenkins, Spinnaker, Bitbucket, GitHub, etc.).

Strong knowledge of cloud security best practices, shift-left methodologies, and DevSecOps processes.

Strong programming skills in Golang or Python, with a solid understanding of software development best practices.

Proficiency with cloud infrastructure provisioning tools (Terraform, KRO, Crossplane, etc.).

Experience with logging and monitoring tools (Splunk, Grafana, Datadog, Prometheus, etc.).

Deep understanding of cloud infrastructure design, architecture, and migration strategies.

Demonstrated proficiency with AI-assisted coding workflows, including experience with LLM-powered development tools, spec-driven development methodologies, and prompt engineering for software engineering use cases.

Preferred Qualifications, Capabilities, and Skills

Master's degree in a related field and certifications in Cloud, Kubernetes, or infrastructure-as-code technologies.

Experience implementing multi-cloud architectures and leading end-to-end platform development efforts.

Background in designing and developing scalable AI/ML or Data platforms.

Experience with automation and workflow orchestration for operational efficiency.

Published contributions to open-source or industry-recognized projects.

Hands-on experience building AI agents and MCP servers/integrations at scale using frameworks such as Google ADK, Anthropic SDKs, and standard agent orchestration tooling.

Experience with enhancing AI-powered coding ecosystems with enterprise specific tooling to improve developer productivity and platform engineering workflows.