Senior Forward Deployed AI Engineer

AZX is hiring a Senior Forward Deployed AI Engineer to scope, build, deploy, and measure AI systems in client environments across utilities, commercial real estate, and logistics. The role partners with client operators, executives, and architects to design solutions, deliver them into client systems, and demonstrate results against agreed metrics.

Responsibilities:

• Own your client's technical delivery end to end: discovery support, solution design, build, deployment into the client's environment, and a handover their team can actually run
• Build the trust machinery behind every system you ship: eval harnesses, replay loops, guardrails, cost/latency budgets, monitoring, and a defined "what happens when it's unsure" path
• Extract structured facts from messy documents (like contractor bids or engineering forms) and build the deterministic checks that gate money- or compliance-sensitive answers, making "cannot determine" fail closed rather than open
• Optimize pipelines for cost and quality, for example, moving a step from a frontier model to a fine-tuned small model or a deterministic rule, then proving quality held with a replay harness
• Design and ship high-stakes systems like after-hours voicemail triage, defining the right autonomy boundary for the risk involved
• Agree on and track the measurement story with the client (KPIs, baselines, and instrumentation for cost, performance, and quality) in writing before deployment and validated after
• Maintain client-facing engineering presence: working sessions with their IT/security teams, demos, POCs that derisk the next engagement, and a feedback loop that carries field-learned requirements back to the platform team

Requirements:

• Applicants must be currently authorized to work in the United States on a full-time basis
• 5+ years of full-stack delivery skills: Python/FastAPI backends, React/TypeScript front ends, deployment, monitoring, real test coverage, and careful data handling since your pod is the whole team
• Experience shipping LLM/agentic systems to production users, not prototypes, with structured outputs, tool use, retrieval, guardrails, and an eval loop you can defend to a skeptic
• A well-stocked technical toolkit and the judgment to use it: small task models (OCR, ASR, classification, reranking), classical NLP, fine-tuning/distillation, deterministic rules, caching, and a frontier model only where it earns its cost
• Experience deploying inside someone else's cloud, identity provider, repos, and compliance regime, and negotiating their IT constraints without losing the design
• Strong stakeholder skills: running discovery with front-line operators, delivering executive readouts, and pushing back plainly (with a cheaper or safer alternative already sketched) when the ask is wrong
• Comfort with ownership under ambiguity: given a vague problem and a deadline, you return with a working thing or a good question
• Practical fluency across our stack: Python (async/FastAPI/Pydantic), TypeScript/React, Postgres/pgvector, Redis, LLM provider APIs, RAG/hybrid retrieval, and agent frameworks (LangGraph/AutoGen/CrewAI-class or hand-rolled loops)
• Familiarity with enterprise deployment concerns: Docker, Terraform/Bicep, Azure and/or AWS, enterprise SSO (SAML/OIDC, Entra), and observability/cost tracking
• A track record with enterprise integrations (SharePoint, Salesforce, SAP/ERP-class systems)
• Bachelor's Degree
• Domain exposure to utilities, commercial real estate, or logistics is a plus
• Master's is a plus