MLOps Engineer - AWS Workflow Specialist Location India Gurgaon Bangalore- two days in a month WFO Employment Type 6 months contract Primary Focus Production ML systems MLOps and scalable deployment on AWS for financial applications Immediate Joiners Only Budget 250k per month Yrs of Exp 6 Location - Permanent Remote with Mandatory 2 Days in a month from Gurgaon Bengaluru office Role Summary We are looking for a strong MLOps Engineer AWS Workflow Specialist to design orchestrate and deploy end-to-end machine learning workflows on AWS for financial applications You will productionize models following the Bank s approved patterns to be provided using AWS-native services and robust CI CD to automate the full ML lifecycle from data ingestion to monitored inference Key Responsibilities Convert ML prototypes into robust low-latency services for batch and real-time inference Design and implement feature stores training pipelines and model registries using AWS-native tools Build end-to-end ML pipelines using AWS services e g SageMaker Glue Lambda Step Functions Redshift Design build and deploy end-to-end ML workflows on AWS using SageMaker Pipelines and SageMaker Endpoints Implement secure and compliant AWS integrations using S3 KMS Lambda and Secrets Manager Automate deployments with AWS CI CD tooling CodeBuild CodePipeline and infrastructure-as-code patterns as per Bank standards Orchestrate complex batch and event-driven workflows using Apache Airflow Integrate streaming data and real-time inference triggers using Kafka Optimize cost performance and reliability of production ML workloads on AWS Develop PySpark and SQL transformations to support large-scale financial datasets Ensure data quality reproducibility and observability across training and inference pipelines Implement MLOps practices including CI CD for ML model versioning and automated retraining Set up monitoring for model drift performance degradation and security compliance controls Collaborate with Data Scientists and stakeholders to align ML solutions with business goals Document architecture runbooks and operational guidelines for smooth handover and support Required Skills Qualifications Strong programming skills in Python PySpark and SQL Hands-on experience with AWS services SageMaker Glue Lambda Redshift Step Functions and related ecosystem Hands-on experience designing and deploying SageMaker Pipelines and SageMaker Endpoints for production inference Robust understanding of AWS security and platform services S3 KMS Lambda and Secrets Manager Experience with CI CD automation on AWS using CodeBuild and CodePipeline and related tooling Workflow orchestration experience with Apache Airflow streaming integration exposure with Kafka Expertise in MLOps practices and production deployment of ML models Familiarity with financial data and compliance requirements Strong software engineering fundamentals testing code quality API design performance troubleshooting Preferred Qualifications Experience with SageMaker Pipelines and SageMaker Feature Store Knowledge of streaming inference and event-driven architectures AWS certifications Machine Learning Specialty Solutions Architect are a plus Experience implementing Bank enterprise ML patterns including governance approvals and standardized deployment templates Experience with AWS EMR or Spark on AWS for large-scale data processing