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ML Ops Solution Architect

2 weeks ago

Hyderabad, Telangana, India ValueMomentum Full time US$ 1,25,000 - US$ 1,75,000 per year

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.)