Experience Range: 3 to 5 Years
Notice Period: Immediate Joiners Preferred
Interview Rounds:
- 1 Internal Technical Round
- 1 Client Round (Face-to-Face Mandatory)
Key Responsibilities
- Build and maintain robust MLOps pipelines to automate machine learning workflows from data ingestion to model deployment.
- Develop production-grade CI/CD pipelines for model development and deployment using tools such as Git, Jenkins, and Docker.
- Work with Data Scientists, Data Engineers, and DevOps teams to enable continuous integration and delivery of ML models.
- Use GCP services like BigQuery, Dataproc, and Cloud Composer (Airflow) to orchestrate and scale ML jobs and data pipelines.
- Ensure model versioning, data lineage, and reproducibility of results across various environments.
- Implement monitoring and logging solutions to track ML model performance post-deployment.
- Troubleshoot issues in data pipelines, model drift, or deployment errors in real-time production settings.
Must-Have Skills
- 3–5 years of experience in MLOps and Machine Learning Engineering roles.
- Strong command over Python, especially for ML and data pipeline development.
- Experience with PySpark for handling large-scale distributed data processing.
- Hands-on expertise with key GCP services
- BigQuery – for scalable data warehousing and analytics
- Dataproc – for managed Spark/Hadoop workloads
- Cloud Composer (Airflow) – for orchestration and workflow automation
- Deep understanding of model training, validation, deployment, and monitoring lifecycles.
- Proficiency in setting up and maintaining CI/CD pipelines for ML applications.
- Knowledge of containerization (Docker) and version control (Git).
Good-to-Have Skills
- Exposure to ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with Kubernetes or other orchestration tools.
- Experience working in regulated industries such as BFSI or Healthcare.
Job Types: Full-time, Permanent
Schedule:
Application Question(s):
- Will you be able to attend a inperson interview in Hyderabad
Work Location: In person
