Key Responsibilities
- Design, deploy, and maintain end-to-endML pipelinesfor training, testing, and deploying models in production.
- Automate model versioning, CI/CD, and monitoring using modern MLOps frameworks.
- Implementdata version control, model registry, and automated retraining workflows.
- Monitor model performance, drift, and system reliability in production.
- Collaborate withdata engineeringandDevOpsteams 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 backgroundin ML Ops or DevOps with ML pipeline experience.
- Proficiency inPythonand experience with libraries likeTensorFlow, PyTorch, Scikit-learn.
- Hands-on experience withML pipeline toolssuch asKubeflow, MLflow, Airflow, or TFX.
- Experience withcontainerization and orchestration(Docker, Kubernetes).
- Familiarity withCI/CD tools(GitHub Actions, Jenkins, GitLab CI, etc.).
- Experience withcloud platforms(AWS, Azure, GCP) and their ML services.
- Knowledge ofmonitoring tools(Prometheus, Grafana, ELK, etc.).
- Strong understanding ofdata pipelines,feature stores, andmodel lifecycle management.
Good to Have
- Exposure toLLMOpsorGenAI pipeline management.
- Experience withFeature Store frameworks(Feast, Hopsworks).
- Familiarity withDataBricks, Vertex AI, or SageMaker.
- Understanding ofAPI deploymentandmicroservices architecturefor ML models.
Educational Qualification
- Bachelor's or Master's degree inComputer 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.
