Senior Software Engineer, MLOps

About the Role

We are looking for an MLOps Engineer to design, automate, and manage the end-to-end machine learning lifecycle — from data engineering to continuous monitoring in production. The ideal candidate will collaborate with data scientists, data engineers, and DevOps teams to build scalable, reliable, and secure ML systems.

What You’ll Be Doing

• Data Engineering: Design and maintain reliable data pipelines for data ingestion, cleaning, and transformation.
• Feature Engineering: Automate and manage feature extraction, selection, and storage for consistent model input.
• Model Development: Collaborate with data scientists to streamline model experimentation and development using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Model Training & Validation: Build automated pipelines for model training, hyperparameter tuning, and evaluation.
• Model Registry & Versioning: Track and manage model versions, artifacts, and metadata using MLflow, DVC, or Vertex AI.
• CI/CD for ML Models: Develop continuous integration and delivery pipelines for model deployment and updates.
• Model Deployment & Inference: Set up scalable, low-latency inference workloads using Kubernetes, Docker, or serving tools like KServe, SageMaker, or Vertex AI.
• Production Monitoring: Implement observability for inference services, including latency, throughput, and resource utilization.
• Model Performance Tracking: Monitor accuracy, drift, and data quality in production environments with automated alerts and dashboards.
• Continuous Training (CT): Automate retraining workflows to keep models current with evolving data.
• Security & Compliance: Ensure ML workflows meet organizational and regulatory compliance requirements.
• Optimization & Collaboration: Continuously improve ML infrastructure, cost efficiency, and developer productivity.

We’re Excited if You Have

• Programming & Scripting: Python, Bash, SQL
• ML Frameworks: TensorFlow, PyTorch, Scikit-learn
• MLOps Platforms: MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Azure ML
• Model Serving & Deployment: Docker, Kubernetes, KServe, Seldon, BentoML
• CI/CD Tools: Jenkins, GitHub Actions, GitLab CI, Argo Workflows
• Monitoring & Logging: Prometheus, Grafana, Datadog, ELK Stack
• Version Control & Artifact Management: Git, DVC, MLflow Registry
• Cloud Platforms: AWS, GCP, or Azure (any one or more)

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