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.
