Lead hands‑on technical execution for Agentic AI capabilities across PoC, MVP, and Production, ensuring delivery and production‑grade quality.
Translate architecture and product intent into a clear build plan (components, sequencing, integration approach, technical risks), and drive implementation through the team.
Define and enforce engineering practices: coding standards, PR/review quality bar, branching strategy, and “done” criteria for agentic workflows (including testability and operational readiness).
Implement and guide development of key agentic components such as orchestration logic, tool/API integrations, retrieval patterns, guardrails, and human‑in‑the‑loop flows, ensuring they are reliable and maintainable.
Own technical unblock and troubleshooting: resolve complex defects, integration issues, performance bottlenecks, and environment/build pipeline problems.
Drive release readiness from a technical standpoint: dependency readiness, versioning, deployment checks, rollback considerations, and operational handover inputs.
Ensure vendor‑delivered technical work is aligned to standards through review, integration oversight, and quality controls.
Collaborate with QA/Validation to ensure the solution is testable and traceable, with appropriate evidence generation through the SDLC (unit/integration/system testing hooks, logs, artifacts).
Partner with the Solutions Architect to ensure adherence to enterprise patterns, security/privacy expectations, and architectural constraints, escalating gaps early and proposing practical implementation options.
Post‑release, drive reliability and performance tuning, manage technical debt, and improve observability to support ongoing stability of agentic behaviors in production.
Requirements
8+ years in software engineering roles with demonstrable hands‑on leadership as a senior developer, tech lead, or lead engineer delivering complex systems.
Proven experience leading technical execution across the SDLC, including integration, testing strategy support, and production release readiness.
Experience delivering AI‑enabled or data‑intensive products in regulated or enterprise contexts is strongly preferred.
Bachelor’s degree in a STEM field (Computer Science, Engineering, Data Science, Information Systems, Mathematics, or related discipline), or equivalent practical experience spanning product management, software delivery, and AI/data systems.
Strong engineering fundamentals in software design, APIs/integration, data flows, and distributed systems considerations.
Working knowledge of architecture principles (security, scalability, reliability, maintainability) and the ability to collaborate effectively with architecture teams to align implementation with standards and best practices.
Strong CI/CD and engineering hygiene: automated testing, code review discipline, environment readiness, and operational awareness.