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.
Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration. Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management
Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores. Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes
Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior
Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.
Required qualifications, capabilities and skills
Formal training or certification on software engineering concepts and 8+ years applied experience
Experience architecting and shipping production large language model applications, including agentic workflows and tool integration 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
Familiarity with agentic workflows and frameworks (e.g., LangChain, LangGraph, Autogen, CrewAI and A2A etc). Experience building retrieval-augmented generation solutions (embeddings, semantic search, grounding) using Vector databases and managing prompt lifecycle/versioning
Strong software engineering fundamentals with ability to deliver cloud-native services using containers and serverless designs on AWS. Advanced python programming skills with experience writing production quality code
Build systems using frontier models from OpenAI, Anthropic or others leveraging platforms such as AWS Bedrock / Google Vertex AI or other similar platforms
Proven technical leadership skills, including mentoring, driving architecture decisions, and influencing cross-functional stakeholders
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
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
Preferred qualifications, capabilities and skills
Experience building standardized evaluation harnesses, automated regression suites, and experimentation platforms for large language model systems
Hands-on experience with Kubernetes-based deployment patterns and operational excellence practices for high-availability services
Experience applying privacy, data minimization, and safe AI guardrail patterns in regulated or high-risk environments
Familiarity with context-efficiency optimization techniques and cost governance for large language model workloads. Experience building reusable developer platforms, reference architectures, and technical standards across multiple teams
