Forward Deployed AI Engineer (Senior)
Careers Azx Io · United States
About this role
## About AZX AZX’s mission is to accelerate positive impact in critical industries through AI transformation. We’re a public benefit corporation founded in 2024 and have been profitable from inception. We work with category leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe), and utilities. We focus on challenges in clean energy, decarbonization, climate risk, energy systems, and global economics—and we’re building for long-term success. --- ## About the Role As a **Forward Deployed Engineer**, you’ll join a team that **scopes, builds, deploys, and measures AI systems inside client environments** (utilities, commercial real estate, and logistics). Your software runs in the client’s cloud under their identity provider and toolchain, within their compliance framework, and integrated with their systems of record. The work is **agentic AI with a correctness envelope**—think document intelligence with deterministic, auditable validation where money or compliance is on the line, voice-of-customer AI, cognitive digital twins, and human-in-the-loop workflows. You won’t be handed a finished spec. You’ll work with client operators and executives to find the real problem, design the solution with their architects, build with your pod, and prove impact with numbers both teams stand behind. --- ## Responsibilities - **Own technical delivery end-to-end**: discovery support, solution design, build, deployment into the client environment, and handover to a team that can actually run it. - Build the **trust machinery** behind every system: 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 (e.g., contractor bids, engineering forms) and implement **deterministic checks** that gate money- or compliance-sensitive answers (fail closed when you can’t determine). - **Optimize pipelines for cost and quality**, e.g., moving from frontier models to fine-tuned small models or deterministic rules—then proving quality with replay harnesses. - Design and ship **high-stakes systems** (e.g., after-hours voicemail triage) with clear autonomy boundaries for the risk involved. - Agree on and track the **measurement story** with the client: KPIs, baselines, and instrumentation for cost, performance, and quality—defined in writing before deployment and validated after. - Maintain a **client-facing engineering presence**: working sessions with IT/security teams, demos/POCs to derisk the next engagement, and a feedback loop back to the platform team. --- ## Core Qualifications - **5+ years** of full-stack delivery: Python/FastAPI backends, React/TypeScript front ends, deployment, monitoring, strong test coverage, and careful data handling. - Experience shipping **LLM/agentic systems to production users** (not prototypes): structured outputs, tool use, retrieval, guardrails, and an eval loop you can defend. - A well-stocked toolkit and judgment to use it: small task models (OCR/ASR/classification/reranking), classical NLP, fine-tuning/distillation, deterministic rules, caching, and using frontier models only when they earn their cost. - Experience deploying inside **someone else’s cloud/compliance regime** (identity provider, repos, constraints) and negotiating IT requirements without losing the design. - Strong stakeholder skills: run discovery with operators, deliver executive readouts, and push back clearly (with c
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