Job Description

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.) · Build agentic AI workflows using frameworks such as LangChain, LangGraph, and Haystack , including: o Multi-agent orchestration (planner, retriever, evaluator, executor agents) o Tool-calling, function execution, and system-to-system automation o Memory management (short-term, long-term, and session-based) · 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. 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: o Model/version management and prompt versioning o CI/CD pipelines for GenAI applications o Automated testing (prompt, retrieval, and regression testing) o 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, and evaluation techniques . · Experience building end-to-end GenAI/Agentic AI products , including backend services and frontend web apps . · Hands-on experience with LangChain, LangGraph, n8n, Co-pilot for building modular, agent-based systems. · Practical experience designing multi-agent architectures and orchestrating reasoning and action workflows. · Strong experience with Vector Databases and Graph Databases (Azure AI Search, Neo4j, Databricks Vector DB) for hybrid, semantic and relationship-driven search. · Proven experience implementing RAG pipelines with structured and unstructured enterprise data. · Proficiency in Python, SQL, Spark , and familiarity with additional languages (e.g., JavaScript). · Hands-on experience with PyTorch and TensorFlow . · Experience working with high-performance, large-scale ML systems in production environments · Ability to solve complex problems in language understanding, reasoning, and GenAI system design · Experience deploying GenAI solutions on Azure , including: o Azure Data Factory (ADF) o Databricks o Azure AI Search o Databricks Genie o AI Document Intelligence o App Services, Azure Functions, Bot Framework o Azure Cache for Redis