GenAI Engineer

Company Overview Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

We are seeking a skilled Generative AI Engineer to join our dynamic team who is eager to solve enterprise problems with GenAI. We are embarking upon many critical AI initiatives to help improve employee productivity, developer productivity, and improve business growth. You will be directly involved in innovating and contributing to these highly demanding AI initiatives. You will be responsible for designing, developing, and deploying generative AI applications to solve complex enterprise problems. You are eager to learn, determined to adapt quickly, and comfortable with some ambiguity in requirements. This position is an individual contributor role reporting to Senior Director, Data Platform and ML Platform.

Contribute to the design, development, and operations of the organization's AI platform, spanning LLM infrastructure, agent systems, and AI Platforms like Glean (enterprise search platform) or Gemini Enterprise Apps or Claude Cowork Support the Glean or any other AI platform by driving sharing, agent development, Glean enablement, and collaboration with the vendors on new feature implementation Help build and maintain the LLM gateway (LiteLLM-based), including multi-provider model routing, fallback configuration, caching, cost tracking, FinOps, and guardrail integration (e.g., Amazon Bedrock guardrails) Develop and maintain LLM observability capabilities — prompt/response logging, token and cost attribution, latency tracking, failure mode clustering, hallucination detection, and input drift monitoring Build, test, and iterate on AI agents and agentic workflows, including multi-step tool use, orchestration patterns, error handling, and human-in-the-loop mechanisms Integrate and manage MCP (Model Context Protocol) servers to connect agents and LLM applications with external tools and data sources such as Slack, Jira, databases, and internal APIs Design and execute evaluation frameworks for LLM applications and agents — building golden datasets, implementing LLM-as-judge patterns, running regression tests on prompt and model changes, and reporting on quality metrics Support VectorDB infrastructure including ingestion pipelines, chunking strategies, retrieval quality measurement, and integration with the broader AI platform Maintain infrastructure-as-code (Terraform), CI/CD pipelines (GitHub Actions), and cloud resources (AWS) that underpin the AI platform Collaborate with Data Science, Product, and Engineering teams to understand use cases, resolve platform issues, and continuously improve the developer experience for AI application builders across the organization Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law. What you bring Basic 5+ years of professional experience in software engineering, platform engineering, DevOps,