AI Engineer

We are seeking a highly skilled and passionate AI Engineer to design and build enterprise-grade, production-ready conversational and agentic AI systems that enhance how users interact with enterprise products, services, insights, and recommendations. This role goes beyond traditional chatbots. You will architect and deliver multi-agent, tool-augmented GenAI solutions capable of reasoning, planning, contextual retrieval, and action execution across multiple enterprise data sources and platforms. You will work on secure, scalable, and governed GenAI systems, aligned with enterprise architecture and compliance standards, ensuring reliability, explainability, observability, and continuous improvement in real-world production environments GenAI & Agentic System Development Design, develop, and deploy production-grade GenAI solutions using advanced LLMs (OpenAI APIs such as GPT- 4.1, GPT-4o, etc.) Implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data. Design hybrid search architectures combining Vector DBs and Graph DBs (e.g., Azure AI Search, Neo4j) for semantic, contextual, and relationship-based retrieval. Build agentic AI workflows using frameworks such as LangChain, LangGraph, and Haystack, including Multi-agent orchestration (planner, retriever, evaluator, executor agents) Tool-calling, function execution, and system-to-system automation Memory management (short-term, long-term, and session-based) Enterprise Integration & Cloud Engineering Develop and integrate AI-powered chatbots and agents within the Azure ecosystem, ensuring seamless interoperability with existing platforms and services. Integrate GenAI solutions with enterprise systems using APIs, event-driven architectures, and message brokers. Build secure, scalable backends leveraging Azure App Services, Azure Functions, Bot Framework, Azure Cache for Redis, and related services. Work closely with Cloud, Digital, Data Engineering, and Business teams to drive adoption and real-world impact. Production Readiness, MLOps & LLMOps Apply MLOps / LLMOps best practices across the lifecycle: Model/version management and prompt versioning CI/CD pipelines for GenAI applications Automated testing (prompt, retrieval, and regression testing) Monitoring, logging, and observability for LLM outputs Implement guardrails for safety, hallucination control, data privacy, and responsible AI. Ensure enterprise-grade governance, including access control, auditability, and compliance with internal policies. Performance Optimization & Continuous Improvement Analyze chatbot and agent performance using quantitative and qualitative metrics (accuracy, latency, adoption, task completion). Optimize prompts, retrieval strategies, agent flows, and system performance based on real usage data. Drive continuous enhancement of user experience through experimentation and feedback loops Requirements Strong understanding of LLMs, transformers, embeddings, prompt engineering,…