AI Engineer

Techcarrot Fz-llc; Chennai; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Techcarrot Fz-llc is seeking an AI Engineer to design and deploy enterprise-grade GenAI systems, with multi-agent orchestration, tool-calling, and memory management across Azure. You will build scalable backends and integrate AI bots into complex enterprise workflows, ensuring compliance and reliability.

The role demands deep expertise in LLMs, RAG pipelines, and hybrid search, plus hands-on experience with LangChain, LangGraph, PyTorch, TensorFlow, and Databricks.

Qualifications

• 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: Azure Data Factory (ADF); Databricks; Azure AI Search; Databricks Genie; AI Document Intelligence; App Services, Azure Functions, Bot Framework; Azure Cache for Redis.

Responsibilities

• GenAI Agentic System Development: Design, develop, and deploy production-grade GenAI solutions using advanced LLMs.
• Build agentic AI workflows using LangChain, LangGraph, and Haystack, including multi-agent orchestration; tool-calling, function execution, and memory management.
• Implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data.
• Design hybrid search architectures combining Vector DBs and Graph DBs for semantic and contextual retrieval.
• Enterprise Integration Cloud Engineering: Develop and integrate AI-powered chatbots and agents within the Azure ecosystem, ensuring interoperability with existing platforms.
• 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.
• Work with Cloud, Digital, Data Engineering, and Business teams to drive adoption and real-world impact.
• Production Readiness, MLOps LLMOps: Apply MLOps/LLMOps across lifecycle; CI/CD; automated testing; monitoring and observability; guardrails for safety and privacy.
• Ensure governance, access control, auditability and compliance with internal policies.
• Performance Optimization and Continuous Improvement: Analyze chatbot/agent performance; optimize prompts, retrieval strategies, and agent flows; drive UX improvements through experimentation.

Skills

LLMs

Prompt engineering

LangChain

LangGraph

n8n

Co-pilot

Python

SQL

Spark

PyTorch

TensorFlow

Azure

MLOps

LLMOps

Vector DBs

Graph DBs

APIs

Tools

Azure App Services

Azure Functions

Bot Framework

Azure Cache for Redis

Neo4j

Azure AI Search

Databricks

Databricks Genie

Azure Data Factory

Job description

Job Summary

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.

Responsibilities

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: 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).


• 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: 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 and 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: Azure Data Factory (ADF); Databricks; Azure AI Search; Databricks Genie; AI Document Intelligence; App Services, Azure Functions, Bot Framework; Azure Cache for Redis.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.