AI DevOps Engineer

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.

Work arrangement
No