Poonamallee Chennai Tamil Nadu Devops engineer Jobs – 166 Open Jobs Today

Technical Architect
HCLTech —Chennai, Tamil Nadu
- Azure
- AWS
- Machine learning
3h

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Technical Architect

HCLTech -
Chennai, Tamil Nadu

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Job details

3 hours ago

Qualifications

TensorFlow
CI/CD
Azure
Kubernetes
Software deployment
Git
Tamil
AWS
Docker
Machine learning
Continuous integration
AI
Jenkins
GitLab
Communication skills
Python

Full job description

| Chennai, Tamil NaduNoida, Uttar Pradesh
Job Summary

We are seeking a highly skilled MLOps Architect to lead the design and implementation of scalable AI infrastructure and model lifecycle management solutions.

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: Architect and 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.

Establish model governance protocols including versioning, approval workflows, and compliance checks.

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.

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