3 days ago
Dublin, IrelandMid Level
Responsibilities
- Architect, build, and maintain scalable MLOps platform capabilities for model training, evaluation, registration, deployment, and monitoring.
- Implement CI/CD/CT pipelines for reliable model deployment and automated retraining.
- Establish monitoring, observability, and alerting for data drift, concept drift, latency, throughput, and prediction quality.
- Operationalize LLMs and generative AI systems using LLMOps workflows, prompt engineering platforms, vector databases, and evaluation frameworks.
- Maintain model registries, metadata tracking, and feature stores for auditability, governance, and reuse.
- Provision, scale, and optimize Kubernetes-based ML inference and training clusters.
- Collaborate with data scientists, ML engineers, and software teams to accelerate model handoffs and production delivery.
- Implement automated data quality, model testing, guardrail, responsible AI, and data governance frameworks.
- Troubleshoot and resolve production incidents involving model-serving infrastructure and data streams.
Requirements
- At least 2 years of experience in MLOps, DevOps, or data engineering focused on machine learning infrastructure.
- Experience using Python and shell scripting for automation and infrastructure engineering.
- Experience with Docker, Kubernetes, and Helm.
- Experience building ML deployment pipelines with GitHub Actions, GitLab CI, or Jenkins.
- Experience with MLOps frameworks, model registries, and experiment tracking tools such as MLflow, Kubeflow, BentoML, Triton Inference Server, or Ray.
- Experience operationalizing LLM and generative AI solutions through APIs or self-hosted inference frameworks, including guardrails, vector stores, and evaluation pipelines.
- Experience with infrastructure-as-code tools such as Terraform, CloudFormation, or Ansible.
- Experience with AWS, Azure, or GCP and microservice or serverless architectures.
- Experience with Prometheus, Grafana, Datadog, and ML monitoring frameworks such as Evidently, Arize, or Fiddler.
- Experience with Git, SQL, and database optimization techniques.
- Strong problem-solving, communication, and cross-functional collaboration skills are preferred.
Benefits
- Hybrid work arrangement requiring at least two days per week in the office, with the exact frequency potentially varying by team.
- Docusign provides reasonable accommodations for qualified applicants with disabilities or religious needs during the application process.
Tech Stack
AnsibleAWSAzureDatadogDockerGitGitHub ActionsGitLab CI/CDGoogle Cloud PlatformGrafanaHelmJenkinsKubernetesMLflowPrometheusPythonSQLTerraform
Categories
About DocuSign
DocuSign builds e-signature, contract lifecycle management, and agreement workflow software used by businesses to prepare, sign, and manage contracts. It sells cloud-based subscriptions and APIs that integrate with systems like Salesforce, Microsoft, and Google to automate document workflows and compliance. Founded in 2003 and headquartered in San Francisco, DocuSign is a public company on NASDAQ with over 1.5 million customers in more than 180 countries.