Forward Deployed AI Engineer

Responsibilities

You will embed within client businesses to design, fine-tune, and deploy AI models that solve specific operational challenges. You are responsible for the end-to-end lifecycle of these models, including performance evaluation, cost optimization, and production serving.

Requirements

Candidates must have proven experience shipping and maintaining production-grade AI systems and LLM features. You should be capable of selecting appropriate models, performing fine-tuning, and building evaluation frameworks to ensure high-quality, cost-effective results.

Skills

• AI Engineering
• LLM
• Fine-tuning
• Supervised fine-tuning
• Preference optimization
• LoRA
• Model evaluation
• Inference optimization
• Python
• Data engineering
• API integration
• Software deployment
• System architecture
• Consulting
• ERP systems
• Search optimization

About this role

Lunon is an AI-native consulting firm in New York City, backed by General Catalyst. Most companies don't need another deck about AI. They need an engineer who can walk in, understand the operation, and ship. That's what we do: we embed engineers inside the business and stay until the software runs without us.
Our open-source research system ranks first on DeepResearch Bench.
The team is young and moves fast. Everyone here has a wide skill set, works with the newest tools as they land, and owns what they ship end to end. Nobody hands you a ticket.
You own the model side of an engagement: choose the right model, adapt it to the client's data, prove it works, and make it fast and cheap enough to run every day. You work beside a Forward Deployed Data Engineer who owns the data. First up: helping an ERP company make its product AI-native.
Fully remote, US-based. Lunon is headquartered in New York City.
What you'll do
Pick the right model for each job, from frontier APIs to open-weight models, and know when fine-tuning beats a better prompt.
Fine-tune when it pays off: supervised fine-tuning, preference optimization, and parameter-efficient methods like LoRA.
Build the evaluation harnesses and benchmarks that tell us whether a change made things better.
Take models from training to serving, and tune inference cost, latency, and quality.
Sit with the client's team, find where AI moves their numbers, and say no to work that won't pay back.
What you'll need
Experience matters here. We judge it by what you've shipped and kept running, not by a number of years.
You've fine-tuned or post-trained a model and can explain what worked and what didn't.
You've built evals that changed a decision.
You've shipped LLM features that real users depend on.
You've served a model in production and cared about its cost and latency.
You use AI coding tools every day and judge the result by the system and its output, not the language.
Nice to have
Apply even if none of these are true.
You've built on or integrated with an ERP, CRM, or accounting system.
You've shipped extraction or search over messy business documents.
You've done consulting, agency, or forward-deployed work and know how to be useful in someone else's building on day two.
How we hire
A 30-minute conversation with the team about what you've shipped and what we're building.
A technical deep-dive on one system you owned, from the business problem to production.
A working session on a real client problem with the team.
References, then an offer. We aim to finish in two weeks.
Beware of recruitment fraud. All official correspondence comes from a lunon.ai email address. We will never ask you to share bank account information, cash a check, or purchase equipment as part of the hiring process.

About Lunon

Business Consulting and Services · 2-10 employees · headquartered in New York, NY · founded 2025