Remote Forward-Deployed AI Engineer (Python)

FullStack; Phoenix (AZ); Remote; USD 120,000 - 190,000; Full time

Benefits offered by this job

Health insurance

401(k) w/ match

100% remote

Paid time off

Job summary

FullStack is seeking Forward Deployed Engineers who sit inside the client’s problem, embedding with an enterprise team to architect, build, and sustain AI-enabled solutions. You’ll act as consultant, operator, and engineer, able to validate business cases with executives and ship scalable AI systems within production environments.

Ideal candidates combine deep ML/data and software engineering expertise with a track record of shaping architecture, data sourcing, and retrieval strategies to

Qualifications

• 8–10+ years across ML, data, and software engineering.
• Python-first, production-grade (typed, tested, deployed); working proficiency in TypeScript/Node, .NET/C#, or Java is a plus.
• Track record as ML/Data Architect, Principal Engineer, or Tech Lead.
• Has designed and owned CI/CD for AI/ML systems (LLMOps / MLOps).

Responsibilities

• Discover. Run workshops with business, data, and engineering stakeholders.
• Structure the process and the spec; map current state to executable specs.
• Architect & design end-to-end agentic systems with retrieval architecture.
• Build agents and retrieval services; develop data flows and eval tooling.
• Engineer the delivery system: CI/CD for AI, prompt/versioning, eval suites.
• Operate (Day 2): observability, cost and token economics, governance.
• Consult: defend decisions with CTOs and senior leadership.

Skills

ML Architect

Python

TypeScript

Java

CI/CD for AI

Production-grade services

Tools

Git

Snowflake/BigQuery

Job description

FullStack is seeking Forward Deployed Engineers who sit inside the client’s problem, embedding with an enterprise team to architect, build, and sustain AI-enabled solutions. You’ll act as consultant, operator, and engineer, able to validate business cases with executives and ship scalable AI systems within production environments.

Ideal candidates combine deep ML/data and software engineering expertise with a track record of shaping architecture, data sourcing, and retrieval strategies to