Forward Deployed Engineer
Posted yesterday
The role in short
Embed with enterprise clients to architect, deploy, and scale AI-powered workflows and integrations using Python and agentic frameworks.
ABOUT THE COMPANY
Growth Protocol is an Enterprise Reasoning Platform headquartered in New York. We help enterprises turn strategy into action by combining a neuro-symbolic reasoning core, seamless data activation, and agentic workflows. Our platform reduces execution drag, accelerates growth, and delivers measurable business outcomes for some of the world’s most complex organizations.
JOB DESCRIPTION
We are looking for Forward Deployed Engineers — elite technologists who embed directly with our clients to tackle their most critical growth challenges. In this role, you’ll work side-by-side with executives, engineers, and our Tier 1 consulting partners to design, deploy, and scale AI-powered workflows that transform how enterprises operate.
In this role, you will own the solution end-to-end: uncovering opportunities, architecting integrations, orchestrating AI models, and proving measurable ROI in weeks, not months. This is a high-impact, high-visibility position at the intersection of technology, strategy, and execution.
OBJECTIVES OF THE ROLE
- Lead end-to-end technical delivery for enterprise engagements, from discovery through deployment.
- Embed with client teams to scope high-value workflows, architect integrations, and orchestrate AI models.
- Act as the technical counterpart during onboarding and proof-of-value phases — ensuring fast time-to-value and successful go-lives.
- Partner with internal Product, Data Science, and Backend teams to deliver scalable, explainable, and reliable AI integrations.
- Collaborate with strategic consulting partners to align technical efforts with client business goals and expansion opportunities.
- Capture learnings and build reusable patterns, documentation, and internal playbooks to accelerate future deployments.
- Communicate effectively with senior executives, advocating for optimal AI workflow strategies while gathering feedback for continuous improvement.
- Partner with sales and customer success to ensure smooth onboarding, adoption, and expansion of workflows.
MINIMUM QUALIFICATIONS
- 5+ years in a hybrid technical + client-facing role (e.g., Solution Architect, Forward Deployed Engineer, AI/ML Systems Engineer, Technical Consultant).
- Experience deploying AI/ML workflows or intelligent applications (LLMs, RAG, forecasting, symbolic reasoning, etc.).
- Strong coding background in Python, plus familiarity with GCP, Docker, FastAPI, LangChain/LangGraph, Elasticsearch, Snowflake, BigQuery.
- Experience with data pipelines and distributed systems in production environments.
- Proven ability to translate complex business challenges into structured technical solutions.
- Strong analytical and problem-solving skills, with experience building repeatable, scalable processes.
- Exceptional verbal and written communication skills, including leading executive-level discussions and technical design sessions.
PERKS
- Competitive compensation and equity in a rapidly growing company
- 100% Company Paid Health, Dental, and Vision Insurance
- 401K Matching
- Remote or Hybrid Work Schedule (Office located in Flatiron District, New York, NY)
- Pet-friendly office
This role can be based out of our New York office or performed fully remotely in the United States or Canada.
Prep for this interview
5 questions likely to come up at Growth Protocol, each matched to a line in this posting.
- 1LLMs, RAG and agents
They wrote: “Experience deploying AI/ML workflows or intelligent applications (LLMs, RAG, forecasting, symbolic reasoning, etc.).”
A customer wants a chatbot over their internal documents. How do you build it?
How to answer
Why they ask: RAG is the most common first project. They want the whole path, from messy files to answers people trust.
- Collect and clean the documents; handle permissions per user
- Split them into chunks, embed, and store with source links
- Retrieve, then answer with citations; say “I don't know” when retrieval is weak
- Build a test set of real questions with expected answers before launch
Tip: Spend more time on the data and on testing than on the model.
- 2Cloud, deployment and security
They wrote: “Strong coding background in Python, plus familiarity with GCP, Docker, FastAPI, LangChain/LangGraph, Elasticsearch, Snowflake, BigQuery.”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 3Discovery and scoping
They wrote: “Lead end-to-end technical delivery for enterprise engagements, from discovery through deployment.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 4Integrations and APIs
They wrote: “Embed with client teams to scope high-value workflows, architect integrations, and orchestrate AI models.”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 5Data and SQL
They wrote: “Experience with data pipelines and distributed systems in production environments.”
How would you design a daily pipeline that feeds customer data into our product?
How to answer
Why they ask: Tests basic data engineering: scheduling, failures, re-runs and monitoring.
- Extract with timestamps or change tracking, not full dumps, when possible
- Transform in steps you can re-run without creating duplicates
- Validate row counts and key fields before loading
- Alert a person on failure, with enough detail to fix it
Tip: Make re-running safe (same input, same result) and say how you'd know it failed.
Not the one? Get the next one by email
New roles land here every day. Pick what you want to hear about.
Want the job, not just the posting? Learn how FDEs work.
FDE Info is our free weekly newsletter for forward deployed engineers: tools, field notes from real deployments, and how FDE teams hire and pay.
Free. One email a week. Unsubscribe in one click.
