MLOps

Job summary

The MLOps role involves designing and implementing production-grade model deployment architectures and developing CI/CD pipelines for AI and ML workloads. Key responsibilities include building enterprise AI platforms in cloud environments, automating model processes, and establishing comprehensive model monitoring frameworks to ensure performance and accuracy.

Responsibilities

• Design and implement production-grade model deployment architectures.
• Develop CI/CD and CT (Continuous Training) pipelines for AI and ML workloads.
• Automate model packaging, testing, validation, deployment, and rollback processes.
• Enable scalable serving infrastructure for ML and GenAI applications.
• Support batch, real-time, and streaming inference architectures.
• Build and manage enterprise AI platforms on cloud and hybrid environments.
• Implement containerized AI workloads using Docker and Kubernetes.
• Establish scalable GPU-enabled environments for model training and inference.
• Implement end-to-end model monitoring frameworks.
• Monitor model accuracy, drift, bias, performance, latency, and usage.
• Establish alerting and automated remediation mechanisms.
• Develop AI observability dashboards and operational metrics.
• Lead root cause analysis for AI platform and model performance issues.
• Implement enterprise Feature Store frameworks.
• Establish data lineage and model traceability mechanisms.
• Define data quality controls and validation standards.
• Govern training, validation, and inference datasets.
• Ensure reproducibility and auditability across AI pipelines.
• Operationalize GenAI and LLM-based solutions.
• Build deployment frameworks for RAG, Agentic AI, and LLM applications.

Skills

Relocation