Senior MLOps Engineer

• Design, build, and maintain scalable MLOps platforms for training, deploying, monitoring, and managing machine learning models.
  
• Develop and optimize CI/CD pipelines for ML applications using GitLab CI, Jenkins etc.
  
• Containerize applications using Docker and orchestrate workloads using Kubernetes.
  
• Build and maintain ML pipelines using Kubeflow, MLflow, Airflow, Argo Workflows, or similar orchestration tools.
  
• Implement model versioning, experiment tracking, model registry, and reproducible ML workflows.
  
• Deploy ML models as REST/gRPC APIs using FastAPI, Flask, or similar frameworks.
  
• Configure monitoring, logging, and alerting using Prometheus, Grafana, Dynatrace, or Splunk.
  
• Optimize model serving performance, scalability, and resource utilization.
  
• Work closely with Data Scientists, Data Engineers, Platform Engineers, and Software Developers to productionize ML solutions.
  
• Ensure platform security, governance, and compliance following DevSecOps best practices.
  
• Troubleshoot production issues and perform root cause analysis for ML workloads.
  

Required Technical Skills

• Strong programming experience in Python.
  
• Hands-on experience with Docker, Kubernetes, Helm, and containerized deployments.
  
• Strong knowledge of Git, branching strategies, merge requests, and CI/CD.
  
• Experience with MLflow, Kubeflow, Airflow, or similar MLOps tools.
  
• Experience with Spark/PySpark and distributed data processing.
  
• Knowledge of REST APIs, FastAPI, Flask, and microservices architecture.
  
• Experience with Linux, Bash scripting, and automation.
  
• Familiarity with model monitoring, drift detection, feature stores, and model governance.
  
• Experience working with SQL and NoSQL databases.
  
• Strong understanding of software engineering principles, design patterns, testing, and code quality.
  

Preferred Skills

• Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks such as LangChain or langfuse.
  
• Experience with NVIDIA GPU workloads and model optimization.
  
• Knowledge of Kafka, RabbitMQ, or event-driven architectures.
  
• Exposure to feature stores such as Feast.
  
• Experience with OpenShift, Red Hat ecosystem, or enterprise Kubernetes platforms.
  
• Knowledge of security scanning tools such as Trivy, SonarQube, and vulnerability management.