Head of Forward Deployed Engineering

Elios, Inc.; Northern (KY); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Remote work

Travel opportunities

Job summary

Elios, Inc. is seeking a Head of Forward Deployed Engineering to lead AI deployment work and shape the practice from the ground up. This executive-level role combines people leadership with hands-on technical judgment across multi-client engagements.

In this remote-friendly position, you will standardize deployment processes, vet engineers, and build reusable assets, while maintaining direct client relationships and delivering high-quality outcomes.

Qualifications

• 8+ years in software engineering, solution architecture, or technical delivery.
• Principal-level full-stack engineering judgment and multi-client delivery experience.
• Direct experience in consulting, client services, systems integration, or digital product services.
• Hands-on production AI deployment experience with complex client environments.
• Ability to explain technical tradeoffs to CTOs and business leaders.

Responsibilities

• Define the operating model for AI deployment pods.
• Lead and coach Forward Deployed Engineers across client engagements.
• Own client-facing delivery from technical discovery through deployment and adoption.
• Turn ambiguous client problems into concrete technical plans.
• Review architecture and code when a pod needs senior technical judgment.
• Drive pod health and rescue plans when projects drift or face risk.

Skills

Executive-level communication

Client-facing delivery

People leadership

Senior technical judgment

Full-stack engineering

Operational deployment

Tools

Agentic coding tools

MCP servers

Tool calling

Job description

Job Description

Head of Forward Deployed Engineering

Location: US-based remote | Type: Full-time | Experience: 8+ years

Location and Logistics

US-based remote

Full-time

Occasional client-site travel for kickoffs, discovery, or delivery rescue

This is not a heavy-travel role

About the Company

Our client deploys AI inside real business environments, with teams embedded close to the work and accountable for outcomes. The goal is straightforward: help companies become AI-native by changing how work gets done, not just adding another tool.

The model centers on AI specialists and embedded pods working inside a client's environment, solving problems on the client's stack, and leaving the team stronger than they found it.

About the Role

You'll step into a leadership role focused on building and running AI deployment work, while helping shape the practice as it takes form. This is a ground-floor seat for someone who can set standards, sharpen delivery, and still get hands-on when a project needs senior technical judgment.

In your first 6 months, you'll work closely with the practice leader to standardize how deployments run, vet Forward Deployed Engineers, create pod playbooks, and turn lessons from client work into reusable acceleration assets. Within the first year, you should be able to run pods independently, including client relationship, technical direction, delivery quality, pod health, and rescue plans when work gets difficult.

This is a player-coach role. You'll lead people, but not from a distance. When a project is at risk, you should be ready to take the keyboard, reason through the architecture, debug the system, and help the team stabilize the work.

What You Will Own

Help define the operating model for AI deployment pods

Lead and coach Forward Deployed Engineers and specialists across client engagements

Own client-facing delivery from technical discovery through deployment and adoption

Turn ambiguous client problems into concrete technical plans

Facilitate discovery and diagnostic phases with client stakeholders

Create reusable playbooks, templates, technical patterns, and acceleration assets

Review architecture and code when a pod needs senior technical judgment

Step into projects that are drifting, blocked, or technically fragile

Build trust with clients through clear communication, calm ownership, and follow-through

What You Need to Have Done

8+ years in software engineering, solution architecture, or technical delivery

Principal-level full-stack engineering judgment

Direct experience in consulting, client services, systems integration, digital product services, or another multi-client delivery environment

A track record of owning client-facing technical work from discovery through production

Experience leading engineers without losing your own technical edge

Strong full-stack background across modern web, backend, data, and cloud systems

Production AI experience beyond demos, including agentic workflows, RAG, LLM integrations, evals, observability, latency, cost, and reliability

Current hands-on fluency with AI engineering tools and practices, including daily use of agentic coding tools

Comfort with MCP servers, tool calling, auth-aware integrations, and enterprise system constraints

Executive-level English communication. You should be able to explain a technical tradeoff to a CTO, a VP of Operations, and a non-technical business owner without losing precision.

How You Lead

You're a trusted operator and a servant leader. The specialists in these pods will often be high-agency, intense, and deeply technical. Your job is to give them clarity, protect the client outcome, remove friction, raise the bar, and step in when the situation needs senior judgment.

You should be calm under pressure, direct without being sharp, and rigorous without creating theater. Clients should feel that you understand the business problem. Engineers should feel that you can help them solve the technical one.

What This Is Not

Not a people manager role for someone who no longer wants to code

Not a pure architect role where someone else handles delivery

Not a fit for someone who has only worked inside one product company

Not a fit for someone who uses AI tools without reviewing, refining, and owning the output

Not a role for someone who needs a fully defined playbook before they can lead

What Success Looks Like

In the first 6 months, you'll help create a clearer deployment operating model, stronger screening for Forward Deployed Engineers, practical pod playbooks, and reusable assets that make each engagement faster and better than the last.

Within the first year, you're independently running pods, owning client trust, coaching engineers, and helping the company scale AI deployments without quality slipping.

The opportunity here is clear: help define how embedded teams deploy AI, work directly with the person leading the practice, and turn early client work into a repeatable system for making companies AI-native.