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.

·       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 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.