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Open role · Forward deployed engineering

Forward Deployed Engineer

An agent is a language model plus a harness, and the model is the smallest part. The work is running it inside a real enterprise, on real data, under real governance. That is what you would do here.

Full-timeHybrid, with travel to customer sitesUnited StatesHyderabadBangaloreRegulated industries
01The role

Make the AI work inside a customer’s world.

Most enterprises 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 agents into production inside their environment.

Forward Deployed Engineers are the people who make that real. You embed with the customer, deploy and integrate on top of what they already have, and get it delivering in weeks rather than 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 alongside the customer’s engineering, security and business teams.

02The work

You own the deployment, end to end.

Stand it up in the field

Install, configure and integrate the platform in customer environments across cloud, on-premises and hybrid, from first connection to production go-live and hypercare.

Connect to their systems

Wire the platform into the systems of record and data platforms a customer already runs. 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 model trained on their data, with persistent memory, retrieval, tool use, evaluation and guardrails.

Govern it and prove it

Enforce policy-based control and human approval where it belongs, and deliver the decision lineage that audit and compliance will ask for in a regulated environment.

Debug across the stack

Trace an issue 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 what you learn in the field back to core engineering, and build the reusable connectors and accelerators that make the next deployment land faster.

03You

You have shipped real systems into hard environments.

  • Strong software engineering fundamentals: 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 and legacy systems: you have connected software to systems of record, databases, APIs or middleware before. Familiarity with SAP, ServiceNow, Salesforce, Epic or Snowflake is a plus, not a checklist.
  • Practical AI in production: RAG, agentic and 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 and CI/CD, across cloud and on-premises.
  • Enterprise security literacy: SSO, SAML and OAuth, role-based access control, 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 travel when the work requires it, and you bias toward shipping something real.

Nice to have

  • 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.
04Why it matters

The AI is only worth what we can land.

Everyone talks about enterprise AI. Very few put it into production inside a large, complex, regulated environment, on the customer’s own data and under their own constraints. That is the gap, and this role closes it.

You see your work go live and change how a large enterprise operates. Prove it in one domain in weeks, then expand.

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.

Pay that respects the level

We pay strongly for the responsibility we hand you. No games and no lowball.

Close to the decisions

No layers between you and the people setting direction. You work directly with founders, customers and core engineering.

05Apply

Enterprise AI is not a demo. It is a deployment.

Come stand it up with us. Apply on LinkedIn, or write to us with your background and a few things you have shipped.

careers@nustudio.ai

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