GenAI MLOps Engineer

About the Role

Core Responsibilities (Must-Have)

Pipeline & CI/CD

• Design, build, and operate repeatable ML pipelines (data prep training evaluation deploy) using tools such as Airflow, Prefect, or cloud-native solutions.
• Author automated CI/CD workflows (GitHub Actions, Azure DevOps, or Jenkins) for model code, pipelines, and container builds, including linting and automated tests.

• Model Deployment & Serving

  
  
• Containerize models with Docker; deploy to Kubernetes (AKS/EKS/GKE) or serverless (Cloud Run, Azure Functions).
• Implement safe rollout patterns (canary, blue/green) to minimize risk when updating model versions.

• Monitoring & Alerting

  
  
• Instrument inference endpoints and pipelines with key metrics (latency, throughput) and logs.
• Create dashboards and alerts (Prometheus/Grafana or cloud-native alternatives) to detect errors, drift, and performance regressions.

• Cloud & Infrastructure

  
  
• Operate core compute resources on one major cloud platform (Azure Databricks, AWS SageMaker, or GCP Vertex AI).
• Write and maintain basic Infrastructure-as-Code (Terraform, or CloudFormation) for provisioning clusters and managed services.

• GenAI Orchestration & Vector Retrieval

  
  
• Use orchestration framework (e.g., LangGraph, Langfuse etc) to automate GenAI workflows.
• Support embedding-based retrieval pipelines: collaborate on vector index maintenance and refresh processes.

• Collaboration & Documentation

  
  
• Work with data science to integrate new models into production.
• Produce clear runbooks, architecture diagrams, and “on-call” guides.

Qualifications

• 5 years in DevOps/MLOps roles, including at least 3 years supporting ML or deep-learning systems.
• Hands-on with one major cloud (Azure/AWS/GCP) and experience provisioning compute for training/inference.
• Strong skills in Docker and Kubernetes or serverless deployments.
• Proven ability to author CI/CD pipelines and IaC.
• Experience with monitoring stacks (Prometheus/Grafana, Datadog, or cloud-native tools).
• Familiarity with a prompt-orchestration framework (e.g., LangChain) and core vector-retrieval concepts.

Soft Skills

• Effective communicator who can translate technical details to cross-functional teams.
• Strong problem-solver who can troubleshoot across data, model, and infrastructure layers.
• Eager to learn new tools and iterate rapidly in a fast-paced environment.

Additional Information

Growth & Nice-to-Have

• Security & Compliance:

   Basic API auth (OAuth/JWT), secrets management (Key Vault, AWS KMS), and data encryption.

• Testing & Validation:

   Data-quality checks (e.g., Great Expectations), adversarial testing, and automated model-quality gates.

• Scalability & Cost Optimization:

   Capacity planning, load testing (Locust, JMeter), spot-instance usage, and caching strategies (Redis).

• Reliability Engineering:

   Participation in on-call rotations, post-mortems, and error-budget SLOs; chaos-testing fundamentals.

• Experimentation Lifecycle:

   Tracking experiments and hyperparameter sweeps (MLflow, Optuna), and supporting A/B tests.

• Tooling Flexibility:

   Familiarity with alternative MLOps frameworks (Kubeflow, TFX) or observability stacks (OpenTelemetry).

• Our Benefits

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)