Docusign
From Docusign's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
What you'll do We are seeking a skilled Generative AI Engineer to join our dynamic team who is eager to solve enterprise problems with GenAI.
Company Overview Docusign brings agreements to life. More than 1.8 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 the Docusign AI-native IAM platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM). What you'll do 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. Responsibility 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, or AI/ML infrastructure Hands-on experience with LLM APIs and working with large language models in production (prompt design, function/tool calling, streaming, structured outputs) Familiarity with LLM orchestration and gateway tools such as LiteLLM, LangChain, or similar frameworks Experience with enterprise search or low-code / no-code AI platforms like Glean or Gemini Enterprise Apps — including management, configuration, and developer support Experience building or operating AI agents or agentic workflows using frameworks like LangGraph, CrewAI, or custom implementations Working knowledge of evaluation approaches for LLM applications — automated test suites, LLM-as-judge, golden dataset management, or A/B comparison infrastructure Proficiency in Python and comfort working across backend services, APIs, and scripting Experience with AWS cloud services and infrastructure-as-code tools such as Terraform Experience building CI/CD pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins Preferred Experience with Model Context Protocol (MCP) — building, integrating, or consuming MCP servers Hands-on experience with LLM observability tooling (Arize, Braintrust, Datadog LLM monitoring, LangSmith, or custom tracing solutions) Familiarity with RAG architectures — embedding models, vector stores, retrieval strategies, and quality evaluation Exposure to prompt injection testing, LLM security, and guardrail implementation Experiencing managing a search platform such as Glean, Gemini Enterprise Apps Familiarity with real-time inference architectures including serverless patterns with AWS Lambda Understanding of semantic caching, intelligent model routing, or FinOps for LLM cost optimization Background in a SaaS or enterprise software environment Strong troubleshooting skills and the ability to debug across LLM application layers — from prompt behavior to infrastructure Solid communication skills and the ability to work cross-functionally with data scientists, product managers, and engineers Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. AI in Our Hiring Process At Docusign, we embrace technology to enhance efficiency and create a better candidate experience. We may record or transcribe interviews using enterprise AI tools to assist with meeting notes and interview documentation so our teams can stay fully engaged in the conversation. All hiring decisions remain 100% human-led. You have the right to opt out at any time by notifying your recruiter or interview team, with zero impact on your candidacy. We encourage using AI tools to research and prepare beforehand. However, to ensure an authentic evaluation, live AI assistance or external recording bots during interviews are not permitted. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-SA4 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. Responsibility 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 Basic 5+ years of professional experience in software engineering, platform engineering, DevOps, or AI/ML infrastructure Hands-on experience with LLM APIs and working with large language models in production (prompt design, function/tool calling, streaming, structured outputs) Familiarity with LLM orchestration and gateway tools such as LiteLLM, LangChain, or similar frameworks Experience with enterprise search or low-code / no-code AI platforms like Glean or Gemini Enterprise Apps — including management, configuration, and developer support Experience building or operating AI agents or agentic workflows using frameworks like LangGraph, CrewAI, or custom implementations Working knowledge of evaluation approaches for LLM applications — automated test suites, LLM-as-judge, golden dataset management, or A/B comparison infrastructure Proficiency in Python and comfort working across backend services, APIs, and scripting Experience with AWS cloud services and infrastructure-as-code tools such as Terraform Experience building CI/CD pipelines using tools like GitHub Actions, Azure DevOps, or Jenkins Preferred Experience with Model Context Protocol (MCP) — building, integrating, or consuming MCP servers Hands-on experience with LLM observability tooling (Arize, Braintrust, Datadog LLM monitoring, LangSmith, or custom tracing solutions) Familiarity with RAG architectures — embedding models, vector stores, retrieval strategies, and quality evaluation Exposure to prompt injection testing, LLM security, and guardrail implementation Experiencing managing a search platform such as Glean, Gemini Enterprise Apps Familiarity with real-time inference architectures including serverless patterns with AWS Lambda Understanding of semantic caching, intelligent model routing, or FinOps for LLM cost optimization Background in a SaaS or enterprise software environment Strong troubleshooting skills and the ability to debug across LLM application layers — from prompt behavior to infrastructure Solid communication skills and the ability to work cross-functionally with data scientists, product managers, and engineers
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