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 JPMorganChase, 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 Senior Lead Software Engineer, Cloud Platform in the Corporate AI and Machine Learning Data Platforms team, you will design and deliver secure, high-quality technology solutions that support our data and analytics strategy. You will develop cloud-based products, enhance productivity, and enable responsible innovation across the firm. You’ll collaborate with others to harness the power of artificial intelligence and machine learning, creating new opportunities and driving business success. You will have the chance to lead, grow, and make a difference 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 Developer Experience (DevEx) practices and evolve CI/CD and developer tooling.
- Align platform strategy and roadmaps with business priorities and lead cross-functional modernization initiatives.
- Evaluate, integrate, and govern strategic tooling to improve developer experience.
- Collaborate with development teams to identify and eliminate platform bottlenecks.
- Define and promote paved paths and self-service workflows for developers.
- Implement real-time telemetry pipelines and workflows for platform observability and analytics.
- Champion adoption of productivity tools through documentation, training, and engagement with the developer community.
- Standardize use of AI-assisted coding tools and AI-powered development ecosystems.
- Contribute to the design and development of AI agents and Model Context Protocol (MCP) integrations using frameworks such as Google ADK and Anthropic SDKs.
Required Qualifications, Capabilities, and Skills:
- Formal training or certification on software engineering concepts.
- 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.).
- Expertise in Developer Experience (DevEx) and CI/CD tools (Jenkins, Spinnaker, Bitbucket, GitHub, etc.).
- Strong knowledge of cloud security best practices, shift-left methodologies, and DevSecOps processes.
- Programming skills in Golang or Python, with 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.
- Proficiency with AI-assisted coding workflows, including LLM-powered development tools, spec-driven development, and prompt engineering.
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.
- 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 agent orchestration tooling.
- Experience enhancing AI-powered coding ecosystems with enterprise-specific tooling to improve developer productivity and platform engineering workflows.