AI DevOps Engineer

Qualient Technology Solutions UK Limited; India; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Qualient Technology Solutions UK Limited is hiring a DevOps Platform Engineer to design and manage the cloud foundation for Generative AI and Agentic AI applications. The role focuses on infrastructure planning, GPU sizing, and LLMOps pipelines to deliver production-ready platforms.

The ideal candidate combines classic DevOps with AI-specific scalability, cost optimization, and strong communication for client stakeholders in the CPG/FS sector. A Bachelor's degree in CS/IT/Engineering is required.

Qualifications

• 5-7 years of DevOps/Platform Engineering experience with Gen AI/LLM infra exposure.
• Hands-on with at least one major cloud platform (AWS, Azure, or GCP) and AI/ML services.
• Experience sizing GPU-based compute for model inference/fine-tuning; cost-aware planning.

Responsibilities

• Design, size, and manage cloud/infrastructure foundation for Gen AI and Agentic AI solutions.
• Plan infrastructure BOMs, GPU sizing, and LLMOps pipelines; optimize AI workloads for cost and performance.
• Translate Gen AI use cases into production-ready, scalable platforms for clients.
• Collaborate with clients to explain AI infrastructure trade-offs to technical and non-technical stakeholders.

Skills

Gen AI/LLM infra

Cloud engineering

DevOps fundamentals

FinOps for AI

Client-facing communication

Education

Bachelor's degree in Computer Science/IT/Engineering

Tools

Terraform

CloudFormation

Ansible

Docker

Kubernetes

Jenkins

GitHub Actions

Azure DevOps

GitLab CI

SageMaker

Azure AI Foundry/OpenAI Service

Vertex AI

Bedrock

LangChain

LlamaIndex

Semantic Kernel

CrewAI

Pinecone

Weaviate

Milvus

Job description

Job Description: DevOps Platform Engineer - Gen AI & Agentic AI

Location:

Preferably India.

About the Role

We are looking for a DevOps Platform Engineer to design, size, and manage the cloud and infrastructure foundation for Generative AI and Agentic AI solutions, preferably within the Consumer Packaged Goods (CPG), Food & Beverage industry. This role owns the technical backbone that GenAI/Agentic applications run on - from infrastructure planning and BOM creation to GPU/compute sizing, LLMOps pipelines, and continuous optimization of AI workloads.

The ideal candidate understands both classic cloud/DevOps fundamentals and the unique infrastructure demands of LLMs, RAG pipelines, and multi-agent systems - and can independently translate a customer's Gen AI use case into a right-sized, production-ready, cost-optimized platform.

Required Qualifications

• 5-7 years of relevant experience in DevOps, Cloud Engineering, or Platform Engineering, with hands-on exposure to Gen AI/LLM infrastructure (inference hosting, RAG pipelines, agent frameworks, or MLOps/LLMOps).
• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP), including AI/ML-specific services (SageMaker, Azure AI Foundry/OpenAI Service, Vertex AI, Bedrock).
• Experience sizing and costing GPU-based compute for model inference and/or fine-tuning workloads.
• Familiarity with vector databases (Pinecone, Weaviate, Milvus, pgvector, etc.) and RAG pipeline architecture.
• Working knowledge of LLM orchestration/agent frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar) — enough to understand infrastructure and integration implications, not necessarily to build agents.
• Practical experience with Infrastructure-as-Code (Terraform, CloudFormation, Ansible) and container orchestration (Docker, Kubernetes).
• Experience with CI/CD tooling (Jenkins, GitHub Actions, Azure DevOps, GitLab CI) adapted for AI/ML deployment pipelines.
• Understanding of FinOps principles applied to AI workloads - token cost management, GPU utilization tracking, model routing for cost efficiency.
• Experience working with or serving CPG / Food & Beverage industry clients strongly preferred.
• Strong communication skills with the ability to explain AI infrastructure trade-offs to client stakeholders and non-technical audiences.
• Bachelor's degree in Computer Science, Information Technology, Engineering, or related field (or equivalent practical experience).

Preferred Qualifications

• Cloud AI/ML certifications (AWS Machine Learning Specialty, Azure AI Engineer Associate, GCP Professional ML Engineer).
• Experience deploying open-source/self-hosted LLMs (e.g., via vLLM, TGI, Ollama, or similar serving frameworks).
• Exposure to responsible AI/governance practices for infrastructure - access control, data residency, model auditing.
• Prior experience in a client-facing consulting or managed services environment delivering AI-enabled platforms.
• Familiarity with CPG-specific data ecosystems (SAP, Nielsen/IRI, plant/IoT systems) that commonly feed Gen AI use cases.