ML-Ops - Engineer
- Datametica
- 3 - 7 years
- Pune
- 5 days ago
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Job Description
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.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Data EngineerEmployement Type: Full timeContact Details:
Company: DatameticaLocation(s): Pune
Keyskills: MLOps GCP Ci/Cd Machine Learning Operations Python Ml
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