Tech S and T-AI Engineer-ISR-Manager-GDSF02

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Job Description: AI Engineer – Infrastructure & Cloud   Title : AI Engineer Level : Manager Experience : 10-12 years   Role Summary We are looking for an AI Architect to sit at the intersection of Infrastructure and Artificial Intelligence — someone who can design, build, and operationalize AI/LLM/Agentic solutions that solve real infrastructure problems: capacity planning, incident response, observability, automation, cost optimization, and resiliency engineering. This is not a pure research role — it's an applied engineering + architecture role for someone who can code, ship, and integrate AI into production infra workflows.   Key Responsibilities

Architect and deliver AI/ML/LLM-based solutions embedded into infrastructure operations (ITOps/AIOps, self-healing systems, predictive capacity, automated RCA) Design and build agentic AI workflows (multi-step, tool-using agents) for infra automation — ticketing, monitoring, remediation, provisioning Evaluate, fine-tune, and integrate LLMs (open-source and commercial: OpenAI, Anthropic, Azure OpenAI, local/open models) into enterprise infra tooling Build RAG pipelines, vector databases, and knowledge-grounding systems over infra documentation, runbooks, and CMDB data Write production-grade code (Python primarily; scripting in Bash/PowerShell) — this role codes, doesn't just design PPTs Integrate AI solutions with cloud platforms (Azure/AWS/GCP), ITSM tools (ServiceNow), observability stacks (Datadog, Splunk, Prometheus/Grafana), and CI/CD pipelines Define and enforce AI governance guardrails — data privacy, model security, hallucination control, cost/token management Partner with infra leadership to identify high-ROI AI use cases and build the roadmap Mentor infra engineers on AI-adjacent skills; act as the internal AI Center of Excellence lead for the infra vertical Own POC → pilot → production lifecycle for AI initiatives, including MLOps/LLMOps practices

Must-Have Skills

10–12 years in Infrastructure/Cloud engineering or architecture, with the last 3–4 years focused on applied AI/ML/LLM work Strong hands-on coding ability — Python (must), API integration, SDK usage (OpenAI/Anthropic/LangChain/LlamaIndex) Solid understanding of LLM fundamentals: prompting, fine-tuning, embeddings, RAG, context windows, tokens/cost Practical experience building agentic AI systems (LangGraph, AutoGen, CrewAI, or custom orchestration) with tool-calling/function-calling Experience with vector databases (Pinecone, Weaviate, FAISS, Azure AI Search, etc.) Deep infra background: cloud architecture, networking, virtualization, ITSM, monitoring/observability, automation (Ansible/Terraform) Experience with MLOps/LLMOps — model deployment, monitoring, versioning, cost governance Strong architecture and solutioning skills — can whiteboard an end-to-end system and defend design tradeoffs

Good-to-Have

Certifications: AWS/Azure/GCP AI or Solutions Architect Exposure to enterprise AI governance/responsible AI frameworks Experience presenting to CXO/leadership on AI strategy and business cases Prior experience in a Big 4 / GDS / large enterprise infra environment Open-source contributions or published POCs in AI/agentic systems

Soft Skills

Ability to translate ambiguous infra pain points into AI-solvable use cases Strong stakeholder management — bridges infra teams, data science teams, and business Comfortable being hands-on (code/architecture) as well as strategic (roadmap, governance)

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