Job Summary

JD :Job Title: MLOps
Offshore Employment Type: Full
Experience Level: 8 Plus years with at least 3 years in MLOps and AI infrastructure Role
Overview:
We are seeking a highly skilled MLOps Technical 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: 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.

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).
Orchestrating retrieval pipelines (vector stores/embeddings), managing prompt/policy configurations, setting SLOs for latency, throughput, and cost,
and instrumenting telemetry for drift, toxicity, hallucinations, and privacy.
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. To be responsible for managing technology in complex projects ,providing technical guidance and ensuring successful delivery of solutions.

 

Key Responsibilities

1. To be responsible for providing technical guidance to a team of developers, enhancing their technical capabilities and increasing productivity.
2. To conduct comprehensive code reviews, establish and oversee quality assurance processes, performance optimization , implementation of best practices and coding standards to ensure successful delivery of complex projects.
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To collaborate with stakeholders to define project scope, objectives, deliverables and accordingly prepare and submit status reports for minimizing exposure & closure of escalations.

Skill Requirements

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Other Requirements

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