The Position
Forward Deployed Engineers sit inside the client's problem, not next to it. You embed with an enterprise team, find the work that actually warrants AI, architect the system, build it, and stay long enough to make it survive contact with production.
This is a three-way role: consultant, operator, engineer. You should be as comfortable pressure-testing a business case with a COO or VP of Engineering as you are diagnosing why retrieval quality collapsed after a data refresh. The people who succeed here can hold a discovery conversation on Monday and ship what they scoped on Thursday.
On this track, your edge is data, retrieval, and model behavior: you make agents trustworthy by getting the data, context, and evaluation right.
We work with regulated industries and Fortune 500 clients who have AI mandates, real constraints, and low tolerance for demos that don't hold up.
What We Are Looking For
Engineering foundation- 8-10+ years across ML, data, and software engineering.
- Python-first, production-grade (typed, tested, deployed services - not notebooks only); working proficiency in TypeScript / Node, .NET / C#, or Java is a plus.
- Track record as an ML/Data Architect, Principal Engineer, or Tech Lead - can assess a data and AI current state and define a credible roadmap.
- Has designed and owned CI/CD for AI or ML systems (LLMOps / MLOps), not just application code handed off to others.
Applied / operational AI- Has put LLM and agent systems into production and kept them running - not just proofs of concept or research models.
- Deep fluency with retrieval and model behavior: embeddings, vector and hybrid search, reranking, structured output, model selection and routing; sound judgment on fine-tuning vs. prompting vs. retrieval.
- Fluent with agentic tooling: Claude Code, agent frameworks, MCP, tool/function calling, multi-agent orchestration.
- Context and spec engineering: designs what the model sees - retrieval, context construction, specifications, process definitions, and memory - for reliable, context-efficient output.
- Day 2 experience: evaluation design (golden sets, LLM-as-judge, groundedness), observability, cost and token economics, failure-mode analysis.
- Data and integration literacy: familiar with ETL/ELT patterns, warehouses/lakehouses (Snowflake, Databricks, BigQuery), and integration options (APIs, event streams, iPaaS, MCP) - enough to design around them, assess data readiness, and guide the data team. Does not need to be an ETL/ELT specialist.
Consulting & business- Demonstrates the discovery behaviors above: business and personal drivers, constraints, stakeholder alignment, strategic questioning.
- Runs client conversations without an account manager in the room: workshops, proposal walkthroughs, hard questions, pushback.
- Translates technical decisions into business outcomes - accuracy, cycle time, cost per transaction, risk reduction, ROI.
- Exceptional written and verbal communication; credible in front of senior stakeholders.
What We Offer
- Competitive Salary.
- Paid Time Off (vacation, sick leave, parental leave, holidays).
- 100% remote work.
- The ability to work with leading startups and Fortune 500 companies.
- Health, dental, and vision insurance.
- 401(k) w/ 4% match.
- Ample opportunity for career advancement.
- Continuing education opportunities.
