Director | MLOps Specialist | AWS Cloud

4 days ago

Hyderabad, Telangana, India Michael Page Full time
Job Description

- Excellent opportunity
- Fast track growth

Job Description

.
12+ years in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelines
.
Strong experience with Amazon Web Services (AWS), especially:
o
Amazon SageMaker (training, deployment, Pipelines, Model Monitor)
o
S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatch
.
Proficiency in Python, Bash, and scripting for automation
.
Familiarity with CI/CD tools like Jenkins, GitHub Actions, CodeBuild, etc.
.
Experience with Docker and container orchestration in AWS (e.g., ECS, EKS optional)
.
Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring
.
Experience with data versioning and model tracking tools (e.g., MLflow, DVC, SageMaker Model Registry)
.
Excellent communication and collaboration skills

The Successful Applicant

Key Responsibilities
.
Design & Implement MLOps Pipelines
o
Build and maintain robust CI/CD pipelines for ML using Amazon SageMaker Pipelines, CodePipeline, Step Functions, etc.
o
Automate model training, evaluation, deployment, and monitoring processes.
.
Infrastructure & Cloud Management
o
Use Infrastructure-as-Code (IaC) tools (e.g., CloudFormation, Terraform, CDK) to manage reproducible environments.
o
Architect scalable ML infrastructure using AWS (e.g., S3, Lambda, ECR, EC2, SageMaker).
.
Monitoring, Logging & Observability
o
Implement model and data monitoring with SageMaker Model Monitor, CloudWatch, or third-party tools.
o
Set up logging, alerts, and dashboards to ensure model health and performance.
.
Governance & Compliance
o
Manage model registries, lineage tracking, and audit logging to support reproducibility and regulatory compliance.
o
Enable version control and approval workflows for ML assets.
.
Collaboration & Enablement
o
Work closely with data scientists, ML engineers, and DevOps teams to integrate ML workflows into existing infrastructure.
o
Educate and mentor cross-functional teams on MLOps best practices and AWS ML tooling.

What's on Offer

Preferred Qualifications
.
AWS Certification (e.g., AWS Certified Machine Learning - Specialty, Solutions Architect - Professional)
.
Knowledge of ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
.
Experience with multi-environment deployment (dev/test/prod) for ML workflows
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Familiarity with data privacy laws and model governance frameworks (GDPR, HIPAA, etc.)

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