- 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 backend 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

- Implement guardrails for safety, hallucination control, data privacy, and responsible AI
- Ensure enterprise-grade governance, including access control, auditability, and compliance with internal policies

- 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

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, embedding, 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.

- Solid 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: Azure Data Factory (ADF)

- Databricks

- Azure AI Search

- Databricks Genie

- AI Document Intelligence
- App Services, Azure Functions, Bot Framework

- Azure Cache for Redis

📌 AI Engineer (Chennai Metropolitan Area)
🏢 techcarrot
📍 Chennai Metropolitan Area