ML-Ops - Engineer

Job Summary: We are looking for a talented MLOps Engineer with 4-7yrs to help design, build, and manage scalable infrastructure for deploying AI/ML and Generative AI models into production. You will be responsible for implementing and maintaining robust ML pipelines, CI/CD workfl ows, containerized deployments, and model monitoring systems. The ideal candidate will have strong experience in cloud-native MLOps practices, especially on GCP (preferred), and a solid understanding of modern machine learning workfl ows and tools. Roles & Responsibilities: ML Pipelines & Automation Develop and manage end-to-end ML pipelines for data processing, model training, testing, and deployment. Automate model lifecycle using tools like MLfl ow, Kubefl ow, Airfl ow, or Vertex AI Pipelines. Model Deployment & Infrastructure Package and deploy models using Docker, Kubernetes, and cloud-native platforms like GCP Vertex AI, Cloud Run, or SageMaker. Implement CI/CD pipelines using tools such as GitHub Actions, Cloud Build, or Jenkins for continuous model integration and delivery. Monitoring & Performance Optimization Set up monitoring systems for model drift, latency, accuracy, and resource utilization. Implement logging, alerting, and observability using tools like Prometheus, Grafana, or Cloud Logging. Collaboration & Support Work closely with AI/ML Architects, Data Scientists, and Software Engineers to ensure reproducibility, scalability, and reliability of AI solutions. Support deployment of GenAI models and components (e.g., RAG pipelines, LLMs, embedding services). Security, Governance & Compliance Ensure secure and compliant handling of data and model artifacts. Manage model versioning, lineage tracking, and audit logging in accordance with internal policies. Required Skills & Qualifications: 4-7 years of experience in MLOps, DevOps, or ML Engineering roles. Strong programming skills in Python and scripting for automation. Hands-on with MLOps tools: MLfl ow, DVC, TFX, or Kubefl ow. Experience with cloud platforms: GCP (Vertex AI, Cloud Build, Artifact Registry), AWS (SageMaker, Lambda), or Azure ML. Profi ciency with containerization (Docker) and orchestration platforms (Kubernetes, Cloud Run). Solid experience in setting up and managing CI/CD pipelines for machine learning workflows. Familiarity with data pipelines, ETL tools (Airfl ow, Datafl ow), and data validation tools.