DevOps Engineer

Responsibilities

• Key Responsibilities

• AI Cloud Platform Strategy, Architecture & Leadership

• Define and own the enterprise AI Platform architecture, technical vision, and multi-year roadmap across Azure and GCP.

• Establish reference architectures, engineering standards, design patterns, and governance frameworks for AI, ML, Generative AI, and Agentic AI platforms.

• Partner with business stakeholders and technology leaders to align platform capabilities with strategic business objectives.

• Identify emerging AI technologies and drive innovation through platform modernization and continuous capability enhancement.

• Lead architecture reviews and make technology selection decisions across AI platform ecosystems.

• Drive platform maturity across reliability, scalability, security, governance, developer experience, and operational excellence.

• Hands-on AI Platform Engineering

• Remain actively involved in the design and implementation of critical platform components and complex technical solutions.

• Provide technical leadership for cloud engineering teams through architecture reviews, design workshops, code reviews, and troubleshooting activities.

• Prototype and validate new AI platform capabilities, tooling, and architectural patterns.

• Lead resolution of complex platform, infrastructure, and AI workload challenges.

• Contribute to automation, platform engineering, Infrastructure as Code, and cloud-native engineering initiatives.

• MLOps, LLMOps & Generative AI Platforms

• Define enterprise standards for MLOps and LLMOps capabilities, including model lifecycle management, deployment, monitoring, observability, governance, and operational excellence.

• Architect and implement scalable GenAI platform services supporting GPT, Gemini, Claude, Llama, Mistral, and open-source foundation models.

• Design enterprise Retrieval-Augmented Generation (RAG) frameworks, model evaluation platforms, prompt management solutions, and guardrail architectures.

• Establish AI observability frameworks covering performance, hallucination detection, model quality, latency, reliability, and cost optimization.

• Drive Responsible AI adoption through governance controls, model validation, compliance, and risk management frameworks.

• Agentic AI & Intelligent Automation

• Define architecture patterns and implementation standards for Agentic AI and multi-agent systems.

• Build and guide implementation of enterprise AI orchestration platforms using LangGraph, CrewAI, AutoGen, Semantic Kernel, MCP, and emerging frameworks.

• Design secure and governed agent deployment models integrating enterprise systems, APIs, business processes, and knowledge repositories.

• Establish human-in-the-loop governance patterns and operational controls for autonomous AI systems.

• Cloud, Kubernetes & Platform Engineering

• Lead architecture and engineering of enterprise Kubernetes platforms on AKS and GKE supporting AI training and inference workloads.

• Design scalable GPU-enabled infrastructure supporting large-scale AI and GenAI workloads.

• Establish best practices for workload isolation, autoscaling, networking, disaster recovery, security, observability, and platform resilience.

• Guide implementation of AI serving technologies including KServe, Ray Serve, vLLM, Triton Inference Server, and distributed inference architectures.

• Drive platform automation, self-service capabilities, and developer productivity improvements.

• DevOps, Automation & Reliability Engineering

• Establish Infrastructure as Code and platform automation standards using Terraform and cloud-native tooling.

• Lead implementation of CI/CD, GitOps, and deployment automation for AI and ML platforms.

• Drive Site Reliability Engineering (SRE) practices, platform observability, operational readiness, and continuous improvement initiatives.

• Define platform KPIs, SLAs, SLOs, and engineering health metrics.