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Atomic North
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Senior MLOps Engineer

Senior MLOps Engineer

Atomic North

8 years

0 Lacs

pune maharashtra india

Posted:2 days ago| Platform:

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Skills Required

ml deployment learning aws automation management model training monitoring design sagemaker automate data experimentation versioning code jenkins gitlab logging governance compliance devops engineering reliability orchestration audit git

Work Mode

On-site

Job Type

Full Time

Job Description

🚀 Hiring: MLOps Engineer

Experience:

Location:

About the Role

skilled MLOps Engineer

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.

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