Design, build, and deploy AI-powered capabilities across the SDLC, including spec-driven development workflows that translate well-formed specifications into secure, verifiable implementations.
Assure AI-generated code quality through guardrails, policy enforcement, and verification for code produced by AI assistants and agents.
Apply SDLC and agent tooling skills to perform automated security checks (design review, dependency and supply-chain analysis, static/dynamic analysis orchestration, and release audit support).
Integrate solutions with enterprise systems (source control, CI/CD, ticketing, security scanning, identity, and internal platforms) using APIs, webhooks, and protocols such as MCP (Model Context Protocol).
Partner with engineering, security, product, and leadership stakeholders to define requirements, evaluate trade-offs, and support solution adoption while applying sound architecture and systems design practices (service boundaries, data models, secure defaults, observability, and extensibility).
What's Needed?
Demonstrated experience developing and deploying AI-based solutions in production environments with measurable business or operational impact.
Strong programming proficiency (e.g., Python, TypeScript/JavaScript, Go, or similar) and adherence to software engineering best practices, including testing, code quality, and maintainability.
Hands-on experience with modern AI/LLM development, including context engineering, agentic system design (agent loop engineering, multi-agent orchestration, tool/function calling), and context window management (token budgeting, cost and latency optimization).
Solid understanding of software architecture and systems design, including API design, event-driven patterns, and data modeling for scalability and extensibility.
Demonstrated ability to work independently across the full delivery lifecycle—requirements analysis, solution design, implementation, deployment, and stakeholder engagement—with accountability for results