Machine Learning Engineer

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