Job Summary
Experience Level: 5 Plus years with at least 3 years in MLOps and AI infrastructure Role Overview:
The ideal candidate will have deep expertise in CICD pipelines for machine learning, model governance, and operationalizing AI systems with traceability,
rollback, and auditability.
Focus Areas: AI Infrastructure Design and Deployment Model Lifecycle Management CICD for Machine Learning Pipelines Model Governance and Compliance
Traceability, Rollback, and Auditability Enablement
Key Responsibilities
Key Responsibilities: implement robust MLOps frameworks to support scalable AI/ML model deployment and lifecycle management.
Design and maintain CI/CD pipelines tailored for machine learning workflows, ensuring seamless integration with data engineering and DevOps practices.
Enable traceability across the ML lifecycle from data ingestion to model deployment and monitoring.
Implement rollback mechanisms and audit trails to ensure reliability and accountability in model operations.
Collaborate with Data Scientists, ML Engineers, and DevOps teams to align infrastructure with business and technical requirements.
Evaluate and integrate tools and platforms for model monitoring, drift detection, and performance tracking.
Ensure security, scalability, and cost efficiency of AI infrastructure across cloud and hybrid environments.
Skill Requirements
Required Skills and Qualifications: Proven experience in MLOps, AI infrastructure, and ML model deployment.
Strong understanding of CI/CD tools (e.g., Jenkins, GitLab CI, Azure DevOps) and ML platforms (e.g., MLflow, Kubeflow, SageMaker).
Hands on experience with containerization (Docker), orchestration (Kubernetes), and cloud services (AWS, Azure, GCP).
Familiarity with model governance frameworks and compliance standards.
Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit learn).
Excellent problem solving and communication skills.