MLOps Engineer - AWS Workflow Specialist (Bengaluru)

MLOps Engineer - AWS Workflow Specialist (Bengaluru)

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


· Solid 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.