Senior MLOps Engineer

Hiring : MLOps Engineer

Experience :

5–8 Years (with at least 2+ years in MLOps or ML deployment roles)

Location :

Bengaluru, Bhopal, Gurgaon, Hyderabad, Jaipur, Mumbai, Pune , Chennai

About the Role

We are seeking a

skilled MLOps Engineer

to operationalize and scale machine learning solutions on AWS. The ideal candidate will have strong expertise in cloud infrastructure, automation, and ML lifecycle management — from model training to deployment and monitoring — ensuring efficient and secure ML operations.

Key Responsibilities

Design and implement

end-to-end MLOps pipelines

using AWS SageMaker, Lambda, and Step Functions.

Automate model deployment, monitoring, and retraining workflows for high availability and performance.

Collaborate with data scientists to streamline model experimentation, versioning, and governance.

Manage infrastructure-as-code using

Terraform

or

CloudFormation

for scalable and repeatable environments.

Implement

CI / CD pipelines

for ML models and data workflows using Jenkins, GitLab, or similar tools.

Set up and maintain

monitoring, logging, and alerting

systems with AWS CloudWatch and native services.

Enforce model governance, approval workflows, and compliance practices.

Partner with DevOps and Data Engineering teams to standardize MLOps best practices.

Continuously optimize ML model performance, reliability, and cost efficiency.

Skills & Requirements

5–8 years

of experience, with

2+ years in MLOps or ML deployment

roles.

Strong knowledge of

AWS services : SageMaker, Lambda, S3, Glue, CloudWatch.

Proficient in

Python

and infrastructure automation using

Terraform

or

CloudFormation .

Experience with

CI / CD tools ,

model monitoring , and orchestration (AWS Step Functions, Airflow).

Understanding of

data governance ,

model approval workflows , and

audit practices .

Good to Have

AWS Certifications

(Machine Learning – Specialty, Solutions Architect).

Exposure to

AWS Redshift ,

Athena , or

Feature Stores .

Familiarity with

Docker ,

Kubernetes , or

EKS

for containerized ML deployments.

Experience with

MLflow

or

SageMaker Model Registry

for model management.

Understanding of

feature engineering pipelines

and

data versioning

tools (DVC, Git LFS).

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.