MLOps Engineer

Key Responsibilities

• Design, build, and maintain ML training and deployment pipelines that are reproducible and scalable
• Implement model serving infrastructure for real-time and batch inference workloads
• Build monitoring and alerting systems for model performance, data drift, and system health
• Manage ML experiment tracking, model versioning, and artifact storage
• Automate the end-to-end ML lifecycle from data ingestion through model deployment
• Collaborate with data scientists and ML engineers to productionize their models efficiently
• Optimize compute costs and resource utilization for GPU/CPU training and inference workloads

Required Skills & Experience

• 3+ years of experience in MLOps, DevOps, or infrastructure engineering with ML focus
• Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, or scikit-learn)
• Hands-on experience with ML pipeline orchestration tools (Kubeflow, Airflow, Vertex AI Pipelines, or SageMaker Pipelines)
• Experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads
• Familiarity with cloud ML services on AWS (SageMaker), GCP (Vertex AI), or Azure (Azure ML)
• Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, or BentoML)
• Understanding of CI/CD principles applied to ML systems
• Experience with experiment tracking and model registry tools (MLflow, Weights & Biases, or Neptune)

Nice-to-Have

• Experience with GPU cluster management and distributed training
• Familiarity with feature stores (Feast, Tecton, or Hopsworks)
• Knowledge of data versioning tools (DVC, LakeFS)
• Experience with LLM serving and optimization (vLLM, TensorRT-LLM)
• Infrastructure-as-code experience (Terraform, Pulumi)

Tech Stack

What We Offer

• Competitive salary and equity package
• Flexible remote or hybrid work arrangement
• Health, dental, and vision insurance
• Annual learning and development budget
• Generous PTO policy

Interview Process

1. 1Recruiter phone screen (30 min)
2. 2Technical phone screen covering infrastructure and ML pipeline fundamentals (45 min)
3. 3System design exercise: design an end-to-end ML deployment pipeline (60 min)
4. 4Hands-on coding round: infrastructure or pipeline automation task (60 min)
5. 5Culture fit and team interview with hiring manager and ML team members (45 min)