AI Engineer

techcarrot; Dadri; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

techcarrot is seeking an AI Engineer to design and deploy production-grade GenAI and Agentic AI systems for enterprise products. You will architect multi-agent, tool-augmented solutions with secure, scalable, governed architecture and continuous improvement in production environments.

You will implement RAG pipelines, hybrid search, and memory management while integrating with Azure services and enterprise systems. Strong focus on MLOps, governance, and performance optimization.

Qualifications

• Strong understanding of LLMs, transformers, embedding, 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 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) for hybrid search.
• Proven experience implementing RAG pipelines with structured and unstructured enterprise data.
• Proficiency in Python, SQL, Spark, and familiarity with JavaScript.
• Hands-on experience with PyTorch and TensorFlow in production ML systems.
• Experience deploying GenAI on Azure (ADF, Azure Functions, Bot Framework, Redis).

Responsibilities

• Design, develop, and deploy production-grade GenAI solutions using advanced LLMs.
• Implement Retrieval-Augmented Generation pipelines over enterprise data.
• Design hybrid search architectures for semantic and relationship-based retrieval.
• Build Agentic AI workflows with multi-agent orchestration and tool-calling.
• Ensure security, governance, and compliance in enterprise GenAI deployments.
• Integrate AI systems with Azure cloud services and enterprise platforms.
• Apply MLOps/LLMOps practices: versioning, CI/CD, testing, monitoring.

Skills

LLMs

Transformers

GenAI

LangChain

LangGraph

n8n

Co-pilot

Vector DBs

Graph DBs

Neo4j

Databricks

Python

SQL

Spark

PyTorch

TensorFlow

Azure

JavaScript

Tools

LangChain

LangGraph

n8n

Co-pilot

Azure AI Search

Neo4j

Databricks Vector DB

Job description

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.
• 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.
• 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