Sr. Forward Deployed AI Engineer

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Sr. Forward Deployed AI Engineer

Automated Industries

• New York, New York, United States
• 18h ago

Job Description Job Description **ABOUT THE ROLE** We are a consulting firm that embeds AI talent directly into client organizations to **rebuild how work gets done** .

Forward Deployed Engineers are **production software engineers** full-stack engineers with infrastructure and deployment expertise. As the senior FDE, you are the technical lead of an embedded team inside a client’s AI Engineering organization - designing, building, deploying, and operating production-grade applications and the scalable infrastructure that supports the client’s internal and external AI products, and connecting their systems and software development lifecycle to their AI and agentic tooling.

You should be AI-native and experienced in the software development life cycle (“SDLC”). You read, write, and debug real code at depth; when a production system breaks, you are the person who opens it up and fixes it. You verify rather than trust: you probe where a model or system actually fails, prototype before committing an architecture, and check output against ground truth in production.

You join as a full-time employee of Automated Industries. Client engagements are three months long, with the opportunity to convert to a full-time role with the client if it’s a mutual fit.

What You’ll Do

• Serve as technical lead of the embedded team: own architecture, delivery, and operational quality across the engagement
• Design, build, test, deploy, and operate production-grade applications and the scalable infrastructure behind the client’s internal and external AI products
• Own the deployment story: CI/CD pipelines, release management, and the full software development lifecycle
• Set the reliability bar: instrumentation, monitoring, observability, and incident response for the systems your team ships
• Architect AI systems in production: context management, model deployment, and agentic tooling integrated into the client’s real infrastructure
• Extend and support the client’s existing AI platform work - build on what exists rather than greenfielding
• Mentor the junior Forward Deployed Engineers staffed alongside you; review their code and grow their production judgment
• Work directly with the client’s engineering leadership to translate priorities into shipped, operating systems
• Use Claude Code and AI-assisted development daily, while understanding every line your team ships

What We’re Looking For

• 5-10 years of full-stack engineering experience with production systems you own end to end
• Python; deep experience with AWS and/or Google Cloud Platform. Amazon Bedrock experience is a plus
• Real depth in CI/CD, the SDLC, and site reliability engineering (“SRE”) - you have carried a pager and fixed what woke you up
• Shipped LLM or AI application work: model deployment, context architectures, agents, or evals running in production
• Code fluency deep enough to debug unfamiliar systems down to the root cause and fix them properly
• Technical leadership: you raise a team’s bar through code review, architecture guidance, and example
• Clear communicator: you can defend a technical decision to engineering leadership and teach a workflow to a junior

**BONUS POINTS**

• Amazon Bedrock, agentic frameworks, or AI platform/instrumentation work you have shipped to production
• Infrastructure tooling depth: Terraform, Docker, Kubernetes, observability stacks
• Prior forward-deployed, consulting, or client-embedded delivery experience
• Open-source work or a public trail of things you’ve built

Compensation & Details

• $160,000 - $200,000 annualized salary, commensurate with experience
• Role is for an initial 3-month engagement at dedicated client. There is an opportunity to convert to full-time employment with the client if it’s a mutual fit, or to continue with us leading the next engagement
• Full-time; on-site at the client in New York City Tuesday through Thursday

**To apply, show us something you built and shipped - a deployed system, a pipeline, an eval harness, a repo, or a Claude Code session that ended in production. We care more about what you…