Forward Deployed AI Engineer (Senior)
Summary
Builds AI-driven workflows (RAG, agent pipelines) for clients in climate/energy/logistics, bridging technical delivery with client collaboration. Focuses on shipping solutions fast while ensuring reliability, with occasional frontend work.
About AZX
Our mission is to accelerate positive impact in critical industries through AI transformation.
We’re growing quickly and already work with category-leaders in real estate (CBRE), energy (LevelTen Energy), logistics (Flexe) and utilities.
We’re a public benefit corporation, founded in 2024 and have been profitable from inception.
We work on challenges in clean energy, decarbonization, climate risk, energy systems and global economics. We’re building our company for long term success and aim to build the ultimate place to work if you’re passionate about AI and positive impact.
About the Role
We are seeking a Forward Deployed Engineer to 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, inside their compliance framework, integrated with their systems of record — you bring the right mix of models and tools for the job rather than working around it. 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 of a client's customers, and human-in-the-loop agentic workflows, all measured against real before/after baselines rather than left as shelf-ware. You won't be handed a finished spec — you'll sit with the client's operators and executives to find the real problem, design the solution with their architects, build it with your pod, and prove it worked with numbers both companies stand behind. You bring a strong area of expertise and expect to use it, and work closely with our team across DevOps, infrastructure, data pipelines, front end, and back end as the engagement requires — you are the engineering face of AZX.
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.
Core Qualifications:
5+ years of 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.
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, there's no "someone else's layer."
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)
Domain exposure to utilities, commercial real estate, or logistics is a plus
Bachelor's Degree; Master's is a plus
Why AZX!
Be part of a fast-growing, profitable, mission-driven company with industry-leading clients tackling the massive opportunity of AI transformation in critical industries.
Competitive early-stage startup compensation (based on capabilities, experience, and location)
Bonus eligibility
Health insurance with meaningful coverage for dependents
Flexible paid time off
Equity
Fully remote culture with a cluster of teammates in Seattle
Additional Information:
Must be willing to travel to Seattle area for final interview and travel 2x/year for company summits
Applicants must be currently authorized to work in the United States on a full-time basis.
We are unable to sponsor or take over sponsorship of employment visas at this time.
Please note that our interview process includes a written take-home assignment followed by a live two-hour technical session with our engineering team, so if that format isn't a good fit, we'd ask that you not apply
Next Steps:
If this job sounds like a great fit but don’t check ALL of these qualification boxes, we’d still love to hear from you!
Skills
As published by ashby · 12 questions · 3 written answers
Basics
Name, Email, Resume
Short answers (2)
- Primary Phone Number optional
- Did anyone refer you to AZX? If so, who? optional
Pick from a list (7)
- Are you currently located in North America?
- Do you currently, or will you in future, require visa sponsorship to work in the US?
- Do you have at least 7+ years of professional software engineering experience
- Have you led the technical design and implementation of a significant feature or system from conception to deployment?
- Do you have experience directly mentoring junior engineers on technical design or coding best practices?
- Are you comfortable working on projects where the initial requirements or solutions are highly ambiguous, requiring you to help define them?
- In your last role, how often did you interact directly with non-technical stakeholders (e.g., product managers, clients) to gather requirements or explain technical concepts?
Written answers (3)
- Describe a time you significantly improved the reliability, performance, or maintainability of an existing system. (1-2 sentences)
- In a high-throughput FastAPI application using PostgreSQL, you need to implement a feature that involves writing data to the database, but also performing a relatively slow, external API call (e.g., to a third-party service for enrichment or notification) that doesn't need to block the primary database write operation. Describe your approach to integrating this external API call. Specifically, discuss the technical patterns you would use within the FastAPI application and any considerations for ensuring data consistency or handling failures in the external call.
- At AZX, we're building systems that matter in critical industries like energy, real estate, and infrastructure, often navigating ambiguous problems. Beyond the technical challenge, what aspects of solving these kinds of real-world, high-impact problems truly excite or motivate you? Feel free to share a brief anecdote or personal reflection.