MLOps Engineer - AWS Workflow

Description:

MLOps Engineer - AWS Workflow Specialist

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

(ref:hirist.tech)