MLOps Engineer

Review Criteria:Strong MLOps profile8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computationMust have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatchMust have hands-on Python for pipeline & automation development4+ years of experience in AWS cloud, with recent companies(Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth Preferred:Hands-on in Docker deployments for ML workflows on EKS / ECSExperience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.Experience with CI / CD / CT using GitHub Actions / Jenkins.Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios. Job Specific Criteria:CV Attachment is mandatoryPlease provide CTC Breakup (Fixed + Variable)?Are you okay for F2F round?Have candidate filled the google form? Role & Responsibilities:We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production. Key Responsibilities:Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.Collaborate with data scientists to productionize notebooks, experiments, and model deployments. Ideal Candidate:8+ years in MLOps/DevOps with strong ML pipeline experience.Strong hands-on experience with AWS:Compute/Orchestration: EKS, ECS, EC2, LambdaData: EMR, Glue, S3, Redshift, RDS, Athena, KinesisWorkflow: MWAA/Airflow, Step FunctionsMonitoring: CloudWatch, OpenSearch, GrafanaStrong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).Strong Linux, scripting, and troubleshooting skills.Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows. Education:Master’s degree in computer science, Machine Learning, Data Engineering, or related field.