MLOps Engineer - AWS Workflow Specialist (Bengaluru)

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 robust 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. Strong 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 .