Data Scientist - MLOps

Skip to main content Data Scientist - MLOps hackajob Pune Division, Maharashtra, India Apply Join or sign in to find your next job Join to apply for the Data Scientist - MLOps role at hackajob Email or phone Password Show Forgot password? Sign in Sign in with Email or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Data Scientist - MLOps hackajob Pune Division, Maharashtra, India 2 weeks ago 46 applicants See who hackajob has hired for this role Apply Join or sign in to find your next job Join to apply for the Data Scientist - MLOps role at hackajob Email or phone Password Show Forgot password? Sign in Sign in with Email or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Save Report this job hackajob is collaborating with Barclays to connect them with exceptional professionals for this role.Join Barclays as a Data Scientist -ML Ops, you will be responsible for designing, building, and operationalizing scalable machine learning solutions using robust MLO ps practices. The candidate will work across the end-to-end ML Ops lifecycle—covering deployment, monitoring, and continuous improvement—ensuring production-grade, governed, and efficient ML systems. This role plays a key part in enabling enterprise-scale AI/ML solutions aligned to Barclays’ cloud and Databricks-based ML Ops framework, ensuring consistency, auditability, and faster time-to-value for business use cases.To Be Successful In This Role You Should HaveStrong hands-on experience in ML Ops (model lifecycle management, CI/CD, deployment, monitoring).Experience in building and operationalizing ML models across environments.Understanding of full ML lifecycle including experimentation, training, validation, deployment, and monitoring.Strong proficiency in Python and PySpark.Experience with large-scale data processing and big data ecosystems.Hands-on experience with AWS and/or Databricks platforms.Experience with data pipelines, feature stores, and model registries.Experience with tools such as:MLflow (experiment tracking, registry)Airflow / orchestration toolsDocker / containerizationExperience with AWS services -S3, IAM, CloudWatch, EMR/Glue, etc.Understanding of scalable data platforms (data lakes, data warehouses).Experience with model monitoring, drift detection, and performance tracking.Understanding of data governance, model governance, and compliance requirements.Some Other Highly Valued Skills IncludeExperience with Databricks-native ML Ops capabilities (Unity Catalog, ML flow registry, asset bundles).Exposure to real-time / batch inference pipelines.Knowledge of feature store concepts and...