ML Ops Engineer

2 days ago

Bengaluru, Karnataka, India Agiliad Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Job Title: ML Ops Engineer

Location: Bangalore

Experience: 38 Years

Employment Type: Full-time

About the Role

We are looking for a skilled ML Ops Engineer to design, build, and optimize scalable machine learning (ML) infrastructure and pipelines. You will collaborate with data scientists, ML engineers, and DevOps teams to operationalize ML models for production at scale.

Key Responsibilities

• Design, deploy, and maintain end-to-end ML pipelines for training, testing, and deploying models in production.

• Automate model versioning, CI/CD, and monitoring using modern MLOps frameworks.

• Implement data version control, model registry, and automated retraining workflows.

• Monitor model performance, drift, and system reliability in production.

• Collaborate with data engineering and DevOps teams to ensure smooth integration with production systems.

• Optimize cloud-based ML workflows for scalability and cost-efficiency.

• Ensure compliance, reproducibility, and documentation for ML lifecycle management.

Required Skills and Experience

• Strong background in ML Ops or DevOps with ML pipeline experience.

• Proficiency in Python and experience with libraries like TensorFlow, PyTorch, Scikit-learn.

• Hands-on experience with ML pipeline tools such as Kubeflow, MLflow, Airflow, or TFX.

• Experience with containerization and orchestration (Docker, Kubernetes).

• Familiarity with CI/CD tools (GitHub Actions, Jenkins, GitLab CI, etc.).

• Experience with cloud platforms (AWS, Azure, GCP) and their ML services.

• Knowledge of monitoring tools (Prometheus, Grafana, ELK, etc.).

• Strong understanding of data pipelines, feature stores, and model lifecycle management.

Good to Have

• Exposure to LLMOps or GenAI pipeline management.

• Experience with Feature Store frameworks (Feast, Hopsworks).

• Familiarity with DataBricks, Vertex AI, or SageMaker.

• Understanding of API deployment and microservices architecture for ML models.

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

Why Join Us

• Work on cutting-edge ML and GenAI projects.

• Opportunity to design scalable ML systems from scratch.

• Collaborative, innovation-driven culture.

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