Machine Learning 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). What you'll do We are seeking an experienced MLOps Engineer to join our team and drive the design, implementation, and optimization of our machine learning lifecycle and deployment platform. This role requires deep expertise in MLOps, continuous integration and deployment (CI/CD/CT) for machine learning models, cloud infrastructure orchestration, model monitoring, and applied Large Language Model (LLM) operationalization (LLMOps). This position is an individual contributor role reporting to Machine Learning Engineering Manager. Responsibility Architect, build, and maintain scalable MLOps platform capabilities to automate model training, evaluation, registration, deployment, and monitoring Implement CI/CD/CT (Continuous Integration, Continuous Deployment, Continuous Training) pipelines to ensure seamless, reliable model deployment and automated retraining workflows Establish model monitoring, observability, and alerting frameworks to track data drift, concept drift, latency, throughput, and overall prediction quality in production Operationalize Large Language Models (LLMs) and Generative AI systems into production environments, including LLMOps workflows, prompt engineering platforms, vector databases, and evaluation frameworks Design and maintain model registries, metadata tracking, and feature stores (e.g., Feast, MLflow) to ensure auditability, governance, and reusability across ML teams Provision, scale, and optimize Kubernetes-based machine learning inference and training clusters for high availability, low latency, and cost efficiency Collaborate closely with data scientists, ML engineers, and software teams to streamline model handoffs and accelerate time-to-production for AI capabilities Implement automated data quality, model testing, and guardrail frameworks to enforce responsible AI and data governance practices Troubleshoot and resolve complex production incidents across model serving infrastructure and data streams 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 2+ years of experience in MLOps, DevOps, or Data Engineering with a focus on machine learning infrastructure Experience with Python and shell scripting for automation and infrastructure engineering Experience with containerization and orchestration (Docker, Kubernetes, Helm) Experience building CI/CD/CT pipelines using GitHub Actions, GitLab CI, or Jenkins for ML model deployment Experience with MLOps frameworks, model registries, and experiment tracking tools (e.g., MLflow, Kubeflow, BentoML, Triton Inference Server, Ray) Experience operationalizing LLMs and GenAI solutions (e.g., OpenAI, Azure OpenAI, open-source models) via APIs or self-hosted inference frameworks (vLLM, TGI), including guardrails, vector stores, and evaluation pipelines Experience with Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or Ansible Experience with cloud platforms (AWS, Azure, or GCP) and microservice/serverless architectures Experience with system monitoring (Prometheus, Grafana, Datadog) and ML monitoring frameworks (Evidently, Arize, or Fiddler) Experience with version control (Git), SQL, and database optimization techniques Preferred Excellent problem-solving skills and ability to work with complex, ambiguous requirements Strong communication skills and ability to collaborate across multidisciplinary teams 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. Applicant and Candidate Privacy Notice #LI-Hybrid = Hybrid