Position Name - AI Engineer

Responsibilities
- Design, build, and deploy production-grade LLM solutions, RAG pipelines, and agentic AI systems on cloud platforms (primarily Azure).
- Conduct AI readiness workshops, map business processes, and define automation ROI through intelligent AI solutions.
- Architect and implement multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, and AutoGen.
- Integrate AI solutions with enterprise systems (ServiceNow, Jira, CRMs, ERPs) via REST APIs and webhooks.
- Apply advanced LLM techniques including prompt engineering, Retrieval-Augmented Generation (RAG), LLM fine-tuning, and tool/function calling with secure, scalable design.
- Monitor performance, drive adoption, and iterate based on telemetry, evaluation pipelines, and user feedback.

Client Discovery & Solutioning
- Run AI readiness workshops, map current processes, and quantify automation ROI.
- Translate business requirements into technical agent designs and LLM-based solution architectures.
- Conduct client demos, manage technical Q&A;,



and communicate AI solutions clearly to both technical and non-technical stakeholders.

Agent & Automation Development
- Build conversational and task-oriented AI agents using low/no-code and Python-native agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex).
- Design and implement RAG pipelines with vector stores (ChromaDB, FAISS, Pinecone, Qdrant, OpenSearch) and semantic search over enterprise data.
- Integrate AI solutions with enterprise applications (M365, Jira, ServiceNow, CRMs, ERPs) via APIs and webhooks.
- Implement Model Context Protocol (MCP) servers and clients for standardised, tool-based agentic workflows.
- Apply data-governance guardrails, prompt engineering best practices, and observability/logging across all deployments.

MLOps & Continuous Improvement
- Configure evaluation pipelines, drift monitoring, and automated performance-improvement triggers.
- Analyse usage telemetry and

📌 AI Engineer (India)
🏢 TeamLogicIT
📍 India