Forward Deployed Engineer
An agent is a model plus a harness, and the model is the smallest part. The hard part is running it inside a real enterprise, on real data, under real governance. That is what you will do.
Make our AI work inside a customer's world
Most companies are stuck because their platforms ask them to migrate first. We do not. NuStudio federates across the systems a customer already runs and puts governed AI agents into production inside their environment.
Forward Deployed Engineers are the people who make that real. You embed with enterprise customers to deploy, integrate, and operationalize our platform on top of the systems they already have, and you get it delivering outcomes in weeks, not quarters.
This is not a back-office engineering job and it is not pre-sales. You write production code, design integrations, and make architecture calls in the field, working shoulder to shoulder with the customer's engineering, security, and business teams. You are part builder, part integrator, part trusted advisor.
Own the deployment, end to end
Stand it up in the field
Install, configure, and integrate our platform in customer environments across cloud, on-prem, and hybrid, from first connection to production go-live and hypercare.
Connect to their systems
Wire our platform into a customer's existing systems of record and data platforms. Build the connectors and pipelines that make governed agents useful against real, messy enterprise data.
Run agents in production
Put agents to work with any model, a frontier LLM or a specialist SLM trained on their data, with persistent memory, retrieval, tool use, evaluation, and guardrails.
Govern and prove it
Enforce policy-based control and human-in-the-loop approvals, and deliver full decision lineage for audit and compliance in regulated environments.
Debug across the stack
Trace issues from the model layer through integration middleware down to the source system, in environments you cannot always reproduce locally.
Close the loop with product
Feed field learnings back to core engineering and build reusable connectors and accelerators so the next deployment lands faster.
You've shipped real systems into hard environments
- Strong software engineering fundamentals, with a few years building and shipping production systems. Fluency in Python, Java, Go, or TypeScript, and comfort switching as the environment demands.
- Experience integrating with enterprise or legacy systems and data platforms. You have connected software to systems of record, databases, APIs, or middleware before. Familiarity with tools like SAP, ServiceNow, Salesforce, Epic, or Snowflake is a plus, not a checklist.
- Practical AI in production, with LLMs and SLMs: RAG, agentic or tool-using workflows, evaluation, and guardrails. You do not need to train foundation models. You need to make them work reliably on real data.
- Cloud and operations depth, deploying to AWS, Azure, or GCP, plus containers (Docker/Kubernetes) and CI/CD, across cloud and on-prem.
- Enterprise security literacy: SSO/SAML/OAuth, RBAC, secrets management, encryption, and data governance.
- The field mindset. You are comfortable being dropped into ambiguity at a customer site, you communicate clearly with engineers and executives, you are willing to travel when the work requires it, and you bias toward shipping something real.
- Prior experience as a forward deployed, solutions, delivery, or implementation engineer.
- Domain depth in a regulated industry: healthcare, defense, financial services, insurance, or manufacturing.
- A track record of building internal tooling or accelerators that made future deployments faster.
Our AI is only as valuable as our ability to land it
Everyone talks about enterprise AI. Very few can actually put it into production inside a large, complex, regulated environment, on the customer's own data, under their own constraints. That is the gap we close, and you are the person who closes it.
You will see your work go live and change how a large enterprise actually operates. Prove it in one domain in weeks, then expand.
A small team, real ownership, room to range
You own real surface area
You own whole deployments and make the calls that matter, in the field and in the code. Your work ships and customers feel it.
Range across projects
Small team, wide surface. You move across customers, domains, and the full stack instead of one narrow slice, and pick up range fast.
Compensation that respects the level
We pay strongly for the responsibility we hand you. No games, no lowball.
Close to the decisions
No layers between you and the people setting direction. Work directly with founders, customers, and core engineering, and grow faster for it.
Enterprise AI isn't a demo. It's a deployment.
Come stand it up with us. Apply on LinkedIn, or send a note with your background and a few things you have shipped.
Prefer email? Write us at careers@nustudio.ai with your resume or LinkedIn and a short note.