Forward Deployed Specialist

Elios AI; Phoenix (AZ); On-site; USD 120,000 - 160,000; Full time

Job summary

Elios AI is seeking a Forward Deployed Specialist to join client engagements inside enterprise accounts and translate complex workflows into production AI software. You will split time between scoping problems, coding integrations, and supporting deployments inside regulated environments.

You will embed with customers, design end-to-end LLM-powered solutions, and ensure successful production cutover while maintaining alignment with business goals and security requirements.

Qualifications

• 5+ years building and shipping production software.
• Comfort deploying and operating on AWS, GCP, or Azure, including containers, observability, and debugging code you did not write.
• Integration experience across enterprise systems, data platforms, and identity and auth.

Responsibilities

• Embed with enterprise customers to map workflows, data, and constraints, then design the integration.
• Build and ship LLM-powered applications and agentic workflows across retrieval, tool integration, orchestration, and evaluation.
• Wire into customer systems including legacy APIs, on-prem data stores, SSO, and enterprise auth.
• Run the technical side of pilots and expansions from workshops through production cutover.
• Write evals to determine if a system is ready to hand over.
• Translate between business language and engineering teams and earn trust on both sides.
• Provide feedback to product to reduce heroics in deployments.
• Handle security reviews, data handling requirements, and procurement questions without stalling.

Skills

Python

APIs

Enterprise systems

Client-facing delivery

Tools

AWS

GCP

Azure

Job description

We're hiring a Forward Deployed Specialist to sit inside enterprise accounts and turn AI capability into software that actually runs. You'll spend your weeks split between the customer's problem and the codebase: scoping messy workflows with the people who live in them, building the integration, then staying on it until it holds up in production.

Our client is an applied AI company working with large enterprises in complex, regulated industries where the data is fragmented, the security review is real, and nobody gets to hand-wave the last 20 percent. Forward deployed is not a support function here. It is the delivery model, and the people doing it carry both the technical judgment and the client relationship.

If you've been the engineer who gets pulled into the room because a demo impressed someone and now it has to be real, this is that job with the title to match.

What You'll Do

• Embed with enterprise customers to map their workflows, data, and constraints, then design the thing that actually fits
• Build and ship LLM-powered applications and agentic workflows across retrieval, tool integration, orchestration, and evaluation
• Wire into customer systems that were never built for this, including legacy APIs, on-prem data stores, SSO, and enterprise auth
• Run the technical side of pilots and expansions, from the first workshop through production cutover
• Write evals that tell you the truth about whether a system is good enough to hand over
• Translate between the customer's business language and the engineering team's, and be trusted by both
• Feed real deployment friction back to product so the platform stops requiring heroics
• Handle security reviews, data handling requirements, and procurement questions without stalling the build

Qualifications

• 5+ years building and shipping production software, with strong Python and solid API and systems fundamentals
• Comfort deploying and operating on AWS, GCP, or Azure, including containers, observability, and debugging code you did not write
• Integration experience across enterprise systems, data platforms, and identity and auth

Client-Facing Delivery

• Track record in a customer-facing technical role: solutions engineering, consulting, professional services, or forward deployed work at a product company
• Able to run a room with mixed technical and business stakeholders, executives included, and leave with a decision
• Judgment about scope, meaning you know what to build, what to defer, and when the honest answer is that AI is the wrong tool

Nice to Have

• Experience in regulated environments such as financial services, healthcare, energy, or public sector
• Background with MCP, agent frameworks, or internal tooling that other engineers actually adopted
• Time at a startup where the delivery playbook did not exist yet and you wrote it

Why Join Us

The team is small enough that you will shape how deployments get done, not just execute someone else's runbook. Engineers here sit close to the customer by design, and the bar is set by whether the system is still running and still used three months after go-live, not by whether the pilot demoed well.

You will get real ownership of accounts, direct access to the people building the platform, and the kind of pattern recognition that only comes from putting AI in front of users who are not impressed by it yet.