ML Ops Solution Architect

Job Summary We are seeking an experiencedSr. MLOps Specialistwith deep expertise inAWS servicesandmachine learning deploymentbest 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 usingAmazon 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 withSageMaker Model Monitor ,CloudWatch , or third-party tools. Set up logging, alerts, and dashboards to ensure model health and performance. Governance & Compliance Managemodel 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 oncloud-based ML pipelines Strong experience withAmazon Web Services (AWS) , especially: Amazon SageMaker(training, deployment, Pipelines, Model Monitor) S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatch Proficiency inPython ,Bash , and scripting for automation Familiarity withCI/CD toolslike Jenkins, GitHub Actions, CodeBuild, etc. Experience withDockerand container orchestration in AWS (e.g., ECS, EKS optional) Understanding of ML lifecycle, including feature engineering, training, deployment, and monitoring Experience withdata versioning and model tracking tools(e.g., MLflow, DVC, SageMaker Model Registry) Excellent communication and collaboration skillsPreferred Qualifications AWS Certification (e.g.,AWS Certified Machine Learning – Specialty ,Solutions Architect – Professional ) Knowledge ofML frameworks(e.g., TensorFlow, PyTorch, Scikit-learn) Experience withmulti-environment deployment(dev/test/prod) for ML workflows Familiarity withdata privacy lawsand model governance frameworks (GDPR, HIPAA, etc.)

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