ML Ops Solution Architect

2 weeks ago

Hyderabad, India ValueMomentum Full time

Job Summary

We are seeking an experienced Sr. MLOps Specialist with deep expertise in AWS services and machine learning deployment best practices to design, build, and maintain scalable, secure, and automated ML pipelines. You will play a key role in bridging the gap between data science and engineering teams, driving production readiness of ML models, and ensuring efficient lifecycle management.

Key Responsibilities

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

Required Skills and Qualifications

- 8+ years in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelines
- Strong experience with Amazon Web Services (AWS), especially:
- Amazon SageMaker (training, deployment, Pipelines, Model Monitor)
- 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

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
- Familiarity with data privacy laws and model governance frameworks (GDPR, HIPAA, etc.)

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