Description

Must Have Technical/Functional Skills

•        Strong expertise in AI Ops / MLOps / LLM Ops practices

•        End-to-end model lifecycle management

•        Advanced model monitoring, observability, and alerting frameworks

•        Drift detection, performance tracking, and automated retraining

•        CI/CD and DevSecOps for AI/ML systems

•        Scalable deployment architectures for AI/ML and LLM

•        AI governance, risk, security, and compliance frameworks [

•        AI platform engineering and operational tooling experience

•        Performance optimization (latency, cost, scalability) for AI workload

s •        Strong experience in automation of AI operations workflows

•        Data pipeline integration and ML infrastructure management

•        Cross-functional collaboration with engineering, data, and platform teams

Roles & Responsibilities

•        Define and implement AI Ops / MLOps / LLM Ops strategy for enterprise AI platforms

•        Manage end-to-end AI operations lifecycle (deployment, monitoring, scaling, optimization)

•        Establish model monitoring, observability, and alerting frameworks for production AI systems

•        Implement model lifecycle management (versioning, deployment, retraining, rollback, drift detection)

•        Define and track AI Ops KPIs (performance, reliability, incident reduction, automation efficiency)

•        Ensure high availability, scalability, and performance of AI systems in production

•        Drive adoption of CI/CD and DevSecOps practices for AI/ML systems

•        Implement governance, risk, security, and compliance controls for AI systems

•        Collaborate with AI engineering, data, and platform teams for seamless operation

•        Manage incident response, root-cause analysis, and continuous improvement for AI system

•        Optimize cost, latency, and resource utilization of AI workloads

•        Drive automation of AI operations processes and workflows

Key Skills

Education

Any Gradute