DevOps Engineer

Questhiring; Gurugram District; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Questhiring is seeking a senior leader to own the enterprise AI Platform architecture, vision, and multi-year roadmap across Azure and GCP. You will set reference architectures, governance, and patterns for AI/ML and GenAI services while partnering with business and tech leaders to align capabilities with strategic goals.

You will drive modernization, lead architecture reviews, and guide platform maturity across reliability, security, and developer experience, while prototyping new capabilities

Responsibilities

• Define enterprise AI Platform architecture, vision, and multi-year roadmap across Azure and GCP.
• Establish reference architectures, standards, and governance for AI, ML, GenAI, and Agentic AI platforms.
• Partner with business and technology leaders to align platform capabilities with objectives.
• Identify emerging AI technologies and drive modernization and capability enhancements.
• Lead architecture reviews and technology selection across AI platform ecosystems.
• Drive platform maturity in reliability, scalability, security, governance, and developer experience.
• Provide technical leadership for cloud engineering teams through reviews, workshops, and code reviews.
• Prototype and validate new AI platform capabilities and architectural patterns.
• Lead resolution of complex platform and AI workload challenges.
• Contribute to automation and Infrastructure as Code initiatives.

Skills

AI Platform Architecture

MLOps

GenAI Platforms

Kubernetes

Terraform

Tools

AKS

GKE

KServe

Ray Serve

Triton Inference Server

Job description

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.
• 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.
• 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.
• 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.