Forward Deployed AI Engineer
Responsibilities
Embed on-site or in tight virtual proximity with client teams for the life of the engagement, and identify the highest-leverage workflow to build first, then the next.
Build, test, and harden production-grade integrations (APIs, MCP connectors, data pipelines) directly against client systems.
Own the last mile: deployment, monitoring, user adoption, and iteration based on real usage, not just proof-of-concept handoff.
Run build-measure-learn cycles with client stakeholders, translating ambiguous business problems into scoped technical solutions.
Act as a trusted technical counterpart to client engineering leads and executive sponsors; communicate tradeoffs in business terms.
Contribute reusable patterns, connectors, and accelerators back to HelixAI (Atlas, Nexus, AIRE) for future engagements.
What Good Judgment Looks Like Here
Simplifies complexity fast: turns a messy client environment into a clear, sequenced plan without waiting for a full spec.
Decides soundly under pressure and owns the call, rather than escalating everything or waiting to be told what to do.
Writes production code directly when that's what moves the engagement forward. This isn't an architecture-only or advisory seat.
Spots risk early and adapts, rather than discovering a blocker after it's already cost the engagement time.
Turns what's learned in the field into something reusable, not a one-off fix for one client.
Qualifications
10+ years of professional software engineering experience, including 2+ years building and shipping production AI systems (not prototypes).
Depth in retrieval architecture (RAG), agent orchestration/tool-use patterns, and evaluation pipeline design.
Strong Python proficiency, with production experience using LangChain and/or LangGraph.
Hands-on AWS experience across core compute, storage, and AI/ML services (e.g., Bedrock, Lambda, S3).
Demonstrated ability to work independently and ambiguously in a client-facing, field environment.
Hands-on experience integrating with enterprise systems via APIs, webhooks, or connector frameworks.
Prior experience in a forward-deployed, solutions engineering, or field engineering role.
Nice to Have
Experience with agentic SDLC methodologies and AI-assisted development tooling (Claude Code, Cursor, GitHub Copilot, or equivalents).
Familiarity with RBAC, audit logging, and governance requirements for regulated or enterprise clients.
Background in a specific vertical (financial services, asset management, healthcare, etc.).
