Source description
About the role
Area(s) of responsibility Data & MLOps Engineering
Design and implement end-to-end MLOps pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
Operationalize GenAI and LLM-based solutions , including prompt management, vector databases, embeddings, and agent workflows.
Implement CI/CD pipelines for ML and GenAI workloads using industry best practices.
Enable scalable experimentation, versioning, rollback, and model lifecycle management.
GenAI & Agentic AI Enablement
Build and operate Agentic AI frameworks supporting multi-agent orchestration, tool calling, memory management, and autonomous task execution.
Implement guardrails for GenAI including security, safety, bias detection, hallucination mitigation, and policy enforcement .
Optimize LLM inference performance, latency, cost, and throughput across environments.
Cloud Infrastructure & Platform Engineering
Architect and manage cloud-native ML platforms on Azure and/or AWS.
Leverage cloud services for compute (CPU/GPU), storage, containerization, and orchestration (Kubernetes).
Implement infrastructure-as-code (IaC) and platform automation to support scalable MLOps operations.
Observability, Governance & FinOps
Implement monitoring for model performance, data drift, concept drift, and system health .
Ensure compliance with enterprise standards for data governance, security, auditability, and Responsible AI .
Collaborate with FinOps teams to manage and optimize GenAI and ML platform costs .
Collaboration & Delivery
Work closely with Data Scientists, AI Engineers, Cloud Architects, SRE, and Security teams.
Support production readiness reviews, incident resolution, and continuous improvement initiatives.
Contribute to reusable accelerators, reference architectures, and best practices.
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