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Docusign

Runs electronic signing and agreement workflows

Lead Software Engineer - AI Platform & Agent Infrastructure

Platform EngineeringUpdated on Nov 13
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.
  • 12+ years of experience in Cloud, Platform, or Infrastructure Engineering with a Bachelor's degree, or 8+ years of experience with a Master's degree.
  • Experience architecting and operating LLM-powered or agentic systems in production, not just prototypes or proofs of concept.
  • Experience replacing manual, ticket-driven operations with automated or self-service systems that act on observations, not just dashboards and monitors.
  • Experience designing and operating an LLM gateway or model routing layer across multiple providers, such as OpenAI, Anthropic, Bedrock, or Azure OpenAI.
  • Experience building and operating agent orchestration frameworks and agentic workflows, such as LangGraph, CrewAI, AutoGen, or a custom orchestration runtime, in production.
  • Experience with GPU and inference-serving infrastructure and Kubernetes at scale, such as vLLM, KServe, Triton, model autoscaling, and capacity planning.
  • Experience writing, reviewing, and testing production-quality code in Python and/or Go.
  • Experience with cloud platforms such as AWS, Azure, or GCP, and Infrastructure-as-Code tools such as Terraform or Ansible.
  • Experience setting technical direction across teams and driving adoption of a shared platform.
  • Strong written and verbal communication.
  • Experience with retrieval-augmented generation (RAG) and vector database infrastructure, including embedding pipelines and retrieval services, in production.
  • Experience running LLMOps practices, including model evaluation, prompt/version management, guardrails, and agent monitoring.
  • Experience with containerization and microservices architecture, including Docker and Kubernetes, beyond AI workloads.
  • Understanding of network architecture and security best practices, including VPNs, firewalls, and load balancing, in cloud environments.
  • Experience presenting architecture and roadmaps to senior technical leadership and driving organization-wide adoption.

Responsibilities

  • Own the end-to-end design, build, and integration of the AI/agentic platform, including systems that route, remediate, and provision themselves; the model gateway, agent orchestration, inference serving, and standards used by other teams. Write the code rather than hand it off.
  • Architect, build, and operate a multi-provider LLM gateway and routing layer, such as LiteLLM, Portkey, Bedrock, or equivalent, so requests are routed automatically and securely with cost awareness and reliability.
  • Design and build orchestration layers and frameworks, such as LangGraph, CrewAI, or a custom runtime, and define capability schemas, guardrails, and policy enforcement for autonomous agents.
  • Architect and operate scalable model-serving and GPU infrastructure, including vLLM, KServe/Triton, Kubernetes-based inference serving, autoscaling, and capacity planning, balancing latency, throughput, and cost.
  • Replace manual provisioning and ticket-driven execution with automated, self-service systems so teams can obtain what they need and common failures can resolve without human intervention.
  • Build golden paths for fine-tuning, model/agent registries, evaluation, and safe deployment, including CI/CD for models, prompts, and agents with canary, A/B, and shadow rollouts.
  • Define and promote architecture patterns, security guardrails, and reusable accelerators/reference architectures that reduce time-to-value for teams building on the platform.
  • Partner with product and platform engineering teams to drive adoption of the AI platform, unblock AI/agentic use cases, and translate platform capabilities into team roadmaps.
  • Implement security controls and Zero Trust practices, including SBOMs, image signing, and policy-as-code, partnering with InfoSec to maintain security across model access, data handling, and agent permissions.
  • Establish observability for AI infrastructure, including token spend, model latency, GPU utilization, and agent behavior, and continuously verify that automation performs as intended.

About the Company

Docusign

Runs electronic signing and agreement workflows

Docusign runs electronic signing and agreement management software for businesses, letting people approve contracts online and track the steps around each agreement. Its eSignature product routes documents to signers and records who signed, while contract lifecycle tools organize preparation, negotiation, and approvals. The Intelligent Agreement Management platform stores contracts in a searchable repository and uses AI to extract terms and connect agreement data to business workflows. Customers use its web applications or embed signing through APIs, and pay for subscriptions based on users, features, and document allowances. Docusign was founded in 2003 and is headquartered in San Francisco.

Company size
5,001-10,000
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2003

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