MLOps Engineer - AWS Workflow Specialist (Bengaluru)

MLOps Engineer - AWS Workflow Specialist<\/span><\/b> <\/p> - Location:<\/b> India (Gurgaon) / Bangalore - two days in a month WFO<\/span> <\/span><\/li> - Employment Type: <\/b>6 months contract<\/span> <\/span><\/li> - Primary Focus: <\/span><\/b>Production ML systems, MLOps, and scalable deployment on AWS for financial applications<\/span> <\/span><\/li> - Immediate Joiners Only<\/b><\/span> <\/span><\/li> - Budget<\/b>: 250k per month <\/span><\/li> - Yrs. of Exp: 6 + <\/span><\/span><\/b><\/span> <\/span><\/li> - Location<\/b> <\/span> - Permanent Remote with Mandatory 2 Days in a month from Gurgaon / Bengaluru office <\/span> <\/span><\/li><\/ul> <\/div> <\/span> <\/p> Role Summary<\/span> <\/h1> 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.<\/span> <\/p> Key Responsibilities<\/span> <\/h1> <\/span><\/span><\/span><\/span>Convert ML prototypes into robust, low -latency services for batch and real -time inference.<\/span> <\/p> <\/span><\/span><\/span><\/span>Design and implement feature stores, training pipelines, and model registries using AWS -native tools.<\/span> <\/p> <\/span><\/span><\/span><\/span>Build end -to -end ML pipelines using AWS services (e.g., SageMaker, Glue, Lambda, Step Functions, Redshift).<\/span> <\/p> <\/span><\/span><\/span><\/span>Design, build, and deploy end -to -end ML workflows on AWS using SageMaker Pipelines and SageMaker Endpoints.<\/span> <\/p> <\/span><\/span><\/span><\/span>Implement secure and compliant AWS integrations using S3, KMS, Lambda, and Secrets Manager.<\/span> <\/p> <\/span><\/span><\/span><\/span>Automate deployments with AWS CI/CD tooling (CodeBuild, CodePipeline) and infrastructure -as -code patterns as per Bank standards.<\/span> <\/p> <\/span><\/span><\/span><\/span>Orchestrate complex batch and event -driven workflows using Apache Airflow.<\/span> <\/p> <\/span><\/span><\/span><\/span>Integrate streaming data and real -time inference triggers using Kafka.<\/span> <\/p> <\/span><\/span><\/span><\/span>Optimize cost, performance, and reliability of production ML workloads on AWS.<\/span> <\/p> <\/span><\/span><\/span><\/span>Develop PySpark and SQL transformations to support large -scale financial datasets.<\/span> <\/p> <\/span><\/span><\/span><\/span>Ensure data quality, reproducibility, and observability across training and inference pipelines.<\/span> <\/p> <\/span><\/span><\/span><\/span>Implement MLOps practices including CI/CD for ML, model versioning, and automated retraining.<\/span> <\/p> <\/span><\/span><\/span><\/span>Set up monitoring for model drift, performance degradation, and security/compliance controls.<\/span> <\/p> <\/span><\/span><\/span><\/span>Collaborate with Data Scientists and stakeholders to align ML solutions with business goals.<\/span> <\/p> <\/span><\/span><\/span><\/span>Document architecture, runbooks, and operational guidelines for smooth handover and support.<\/span> <\/p> Required Skills & Qualifications<\/span> <\/h1> <\/span><\/span><\/span><\/span>Strong programming skills in Python, PySpark, and SQL.<\/span> <\/p> <\/span><\/span><\/span><\/span>Hands -on experience with AWS services: SageMaker, Glue, Lambda, Redshift, Step Functions (and related ecosystem).<\/span> <\/p> <\/span><\/span><\/span><\/span>Hands -on experience designing and deploying SageMaker Pipelines and SageMaker Endpoints for production inference.<\/span> <\/p> <\/span><\/span><\/span><\/span>Strong understanding of AWS security and platform services: S3, KMS, Lambda, and Secrets Manager.<\/span> <\/p> <\/span><\/span><\/span><\/span>Experience with CI/CD automation on AWS using CodeBuild and CodePipeline (and related tooling).<\/span> <\/p> <\/span><\/span><\/span><\/span>Workflow orchestration experience with Apache Airflow; streaming integration exposure with Kafka.<\/span> <\/p> <\/span><\/span><\/span><\/span>Expertise in MLOps practices and production deployment of ML models.<\/span> <\/p> <\/span><\/span><\/span><\/span>Familiarity with financial data and compliance requirements.<\/span> <\/p> <\/span><\/span><\/span><\/span>Strong software engineering fundamentals (testing, code quality, API design, performance troubleshooting).<\/span> <\/p> Preferred Qualifications<\/span> <\/h1> <\/span><\/span><\/span><\/span>Experience with SageMaker Pipelines and SageMaker Feature Store.<\/span> <\/p> <\/span><\/span><\/span><\/span>Knowledge of streaming inference and .