Work side by side with clients, PMs, and Architects to scope and deploy AI systems that actually solve problems.
Build and integrate systems using LLMs, RAG pipelines, agent frameworks, vector DBs, and related tools.
Navigate real-world challenges: data access, authentication, security, networking, and brittle enterprise environments.
Translate high-level designs into working components that deliver measurable outcomes.
Debug relentlessly
optimize for reliability in production, not just elegance in code.
Write modular, reusable code that balances speed of delivery with long-term maintainability.
Embed with client engineering teams to accelerate adoption and integration.
Share insights from field implementations to inform reusable components in our platform.
Document decisions with clarity so both clients and internal teams can build on your work.

5+ years of engineering experience, with proven work shipping AI/ML systems into production.
Strong Python skills and deep familiarity with modern AI/ML frameworks (LangChain, LlamaIndex, Hugging Face, vector DBs).
Hands-on with cloud deployment, orchestration, CI/CD, and distributed environments (AWS, Azure, GCP).
Ability to debug gnarly real-world issues (auth failures, networking black holes, flaky data pipelines).
Problem-solver who thrives in ambiguity, focused on delivery and reliability over process and ceremony.
Comfortable in client-facing environments
credible with engineers, clear with executives.

Impact: Ship AI systems that don’t just demo well but run at scale in Fortune 500 enterprises.
Growth: Stay hands-on with cutting-edge frameworks while developing field-tested instincts.
Variety: Solve problems across industries, from finance to healthcare to defense.
Culture: Work in a team that prizes resilience, creativity, and winning over process.
Trajectory: Build both your technical and consulting muscles in one of the most demanding roles in AI delivery.

Forward Deployed AI Engineer

Key skills