Forward Deployed Engineer

Elios, Inc.; Houston (TX); Remote; USD 83,000 - 131,000; Full time

Job summary

Elios, Inc. is hiring Forward Deployed Engineers to operationalize AI prototypes into production-ready solutions on a remote US-based contract basis.

You'll scope problems with clients, implement Python-based LLM applications, connect to client systems, and own deployment across AWS/Azure/GCP while ensuring performance, security, and cost controls. Strong client-facing skills and a track record of shipping real-world AI systems are expected.

Qualifications

• 4+ years of production software experience with Python as a core language.
• Shipped an LLM-powered system to real users (agents, RAG, tool use, or extraction).
• Familiar with cloud deployment, APIs, auth, logging, and CI/CD workflows.
• Ability to evaluate LLM outputs beyond eyeballing results.

Responsibilities

• Translate client goals into scoped, buildable work during discovery.
• Build LLM applications, agents, and retrieval systems in Python.
• Connect models to client systems including APIs and data stores.
• Write evaluations upfront and decide what to fix next.
• Deploy on AWS, Azure, or GCP and own post-launch behavior.
• Explain trade-offs to technical and non-technical audiences.
• Share field learnings with the team to accelerate future engagements.

Skills

Python

LLM systems

Cloud deployment

APIs

CI/CD

LLM evaluation

Client-facing communication

Tools

MCP

Agent frameworks

Orchestration tooling

Job description

Location: Remote (US) | Type: Contract, Full Time | Experience: 4+ years

About the Role

We're hiring Forward Deployed Engineers to take AI from a promising prototype to something people use every day. You'll scope the problem with the client, write the code, connect it to their systems, and stay with it until it works in production.

These are full-time contract engagements with enterprise teams and AI-focused companies in industries like energy, healthcare, financial services, and industrial operations. Some weeks look like backend engineering. Others look like consulting: running a working session with operators, figuring out what the data can actually support, and telling a VP what's realistic.

If you've built an agent or RAG system that impressed people in a demo and then watched it stall before launch, this is the job where you get to finish it.

What You'll Do

• Sit in discovery with client stakeholders and turn loose business goals into scoped, buildable work
• Build LLM applications, agents, and retrieval systems in Python that hold up under real latency, cost, and security limits
• Connect models to the systems the client already runs: APIs, data warehouses, internal tools, and MCP servers where they fit
• Write evals before calling anything done, and use them to decide what to fix next
• Deploy on AWS, Azure, or GCP and own what happens after launch
• Some of the work is integration and cleanup. Better to say that now than surprise you later
• Explain trade-offs to technical and non-technical audiences, in writing and in the room
• Bring what you learn in the field back to the team so the next engagement starts further ahead

Qualifications

• 4+ years writing production software, with Python as a primary language
• Shipped at least one LLM-powered system to real users (agents, RAG, tool use, or structured extraction)
• Comfortable with cloud deployment, APIs, and the unglamorous parts like auth, logging, and CI/CD
• Working knowledge of how to evaluate LLM output, beyond eyeballing it

Client-Facing

• Experience in a customer-facing technical role: consulting, solutions engineering, implementation, or a startup where you talked to users directly
• You can run a meeting, write a clear status update, and push back on a bad requirement

Nice to Have

• Experience with MCP, agent frameworks, or orchestration tooling
• Background in a regulated industry such as healthcare, finance, energy, or government
• You use AI tools daily in your own work and have opinions about which ones are worth it

Why Join Us

You'll work alongside engineers and strategists who've delivered AI across a lot of enterprise environments, so you won't be figuring out client work alone. Engagements are scoped around outcomes, not hours of busywork, and you'll have a team behind you that knows how to run them.