Forward Deployed Engineer

About Serve AI

Serve AI is building the next generation of enterprise intelligence infrastructure for organizations that cannot afford hallucinations, inconsistent outputs, or black box AI. Our deterministic intelligence platform delivers fast, traceable, and auditable answers that organizations can trust across regulated, security conscious, and mission critical environments.

Unlike traditional AI systems that generate probabilistic responses, Serve AI is designed around deterministic execution. Every answer is grounded in verified enterprise knowledge, providing organizations with consistent, explainable, and repeatable results while maintaining complete control over their data and deployment.

We are backed by JAM Fund and a network of investors and executives from leading technology companies including Google, Microsoft, Meta, NVIDIA, OpenAI, Anthropic, DoorDash, Toast, Tesla, CAA, and Major League Baseball.

As an early stage company, every employee has the opportunity to make a meaningful impact. We move quickly, value ownership over bureaucracy, and look for people who enjoy solving difficult problems, building systems from scratch, and helping define the future of enterprise AI. If you thrive in environments where your work directly influences customers, products, and company growth, you'll fit right in.

Forward Deployed Engineers build working systems inside a customer's environment. You'll own named accounts end to end: understanding the customer's data, business objectives, and configuring the platform against it. You are responsible for the final go live acceptance.

• Own deployments end to end. From kickoff to live and accepted, you're the single point of accountability for your named accounts.
• Work directly with customer data teams. Run technical working sessions, get to a shared understanding of their warehouse, and identify the artifacts and access the deployment needs.
• Model the customer's domain. Translate how a business actually talks about its data into a configuration the platform can serve: entity definitions, business logic, terminology, and the questions their team asks.
• Diagnose and resolve accuracy gaps. Identify any gaps. Isolate whether the cause is configuration, data, or platform. Advocate for the fix. Bring suggestions not problems.
• Deploy across environments. Cloud, on-premise, edge, and airgapped,
• Handle customer data and credentials responsibly. You'll hold schemas, sample data, and access for customer systems. Knowledge of proper security procedures and protocols is essential

Deep understanding of SQL.

Python proficiency

Customer facing experience. You've run technical sessions with a customer's data teams.. Solutions engineering, implementation engineering, or deliveryside data engineering all qualify.

Data modeling judgment. You can hold a business domain in your head well enough to know which questions matter

Evaluation literacy. You're comfortable with test sets, measured accuracy, and error analysis, and you report numbers with their sample size. You can tell the difference between a system that's genuinely improved and a metric that moved.

Tolerance for incomplete inputs. Most deployments start with a partial schema and a vague brief.

Written precision. Your defect reports, reference sets and customer summaries are clear and understandable.

• Data warehouse depth: Snowflake, BigQuery, Databricks, Postgres
• Semantic layer, BI, or naturallanguagequery tooling: dbt, LookML, Cube, or a text to SQL system
• Regulated customers: financial services, health, telecommunications, government, defense
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• On premise, airgapped, or sovereign deployment experience