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# Forward Deployed AI Engineer (Senior)

- **Company:** PROVN INC
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Code Coverage, DevOps, Design of User Interfaces, Human-Computer Interaction, Information Retrieval, Python (Programming Language), PostgreSQL, OpenID, Redis, Salesforce.Com, Security Assertion Markup Language (SAML), SAP (Applications), Microsoft SharePoint, Systems Integration, TypeScript, Data Processing, ReactJS, Retrieval-Augmented Generation, Large Language Models, Caching, Agentic-AI, Fastapi, Pgvector, CrewAI, Bicep, AutoGen, Data Management, Terraform, Docker
- **Published:** October 31, 2026
- **Apply:** https://www.careerbuilder.com/job-details/forward-deployed-ai-engineer-senior-seattle-wa--231946a4-6d5c-4cc7-959e-f5fbe8ef9d3c

## About the Role

Artificial Intelligence (AI), Commercial Real Estate, Logistics, Cloud Computing, Artificial Intelligence (AI), System Integration (SI), DevOps, Data Management, User Interface/Experience (UI/UX), Team Player, If this job sounds like a great fit but you don't check all of these boxes, we'd still love to hear from you.

 * 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

Nice to Have

 * Master's degree
 * Domain exposure to utilities, commercial real estate, or logistics

## Description

* 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