ML Ops Solution Architect

2 weeks ago

Hyderabad, India ValueMomentum Full time

Job SummaryWe 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 ResponsibilitiesDesign & Implement MLOps PipelinesBuild 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 ManagementUse 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 & ObservabilityImplement 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 & ComplianceManage model registries , lineage tracking, and audit logging to support reproducibility and regulatory compliance.Enable version control and approval workflows for ML assets.Collaboration & EnablementWork 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 Qualifications8+ years in software engineering, DevOps, or ML Engineering with a focus on cloud-based ML pipelinesStrong experience with Amazon Web Services (AWS) , especially:Amazon SageMaker (training, deployment, Pipelines, Model Monitor)S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatchProficiency in Python , Bash , and scripting for automationFamiliarity 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 monitoringExperience with data versioning and model tracking tools (e.g., MLflow, DVC, SageMaker Model Registry)Excellent communication and collaboration skillsPreferred QualificationsAWS 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 workflowsFamiliarity with data privacy laws and model governance frameworks (GDPR, HIPAA, etc.)

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