AI Engineer

techcarrot; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

techcarrot in Hyderabad, India seeks an AI Engineer to design enterprise-grade GenAI systems, including multi-agent, tool-augmented agents, with secure, scalable architecture and governance.

You will deploy production-ready AI services across Azure, integrate with enterprise data sources, and drive reliability, explainability, and observability in real-world deployments. Collaborating with Cloud, Data, and DevOps teams, you will advance MLops for ongoing improvements.

Qualifications

• Extensive experience with GenAI products and agent-based systems.
• Hands-on with LangChain/LangGraph and orchestration of agents.
• Experience with vector and graph databases for semantic search.
• Production ML systems experience on Azure platform.

Responsibilities

• Design and deploy production-grade GenAI solutions.
• Build multi-agent workflows with tool-calling and execution.
• Implement guardrails for safety, privacy, and governance.
• Collaborate across data, cloud, and engineering teams.

Skills

GenAI

Multi-agent systems

LangChain

LangGraph

Python

Azure

RAG

Vector DBs

Graph DBs

MLOps

Tools

Azure AI Search

Neo4j

Databricks

n8n

Co-pilot

Job description

• We are seeking a highly skilled andpassionate AI Engineer to design and build enterprise-grade,production-ready conversational and Agentic AI systems that enhance howusers interact with enterprise products, services, insights, andrecommendations.
• This role goes beyond traditionalchatbots. You will architect and deliver multi-agent, tool-augmented GenAIsolutions capable of reasoning, planning, contextual retrieval, andaction execution across multiple enterprise data sources and platforms. Youwill work on secure, scalable, and governed GenAI systems , aligned withenterprise architecture and compliance standards, ensuring reliability,explainability, observability, and continuous improvement in real-worldproduction environments

GenAI & Agentic System Development

1. Design,develop, and deploy production-grade GenAI solutions using advanced LLMs(OpenAI APIs such as GPT- 4.1, GPT-4o, etc.
2. Implement Retrieval-AugmentedGeneration (RAG) pipelines using structured and unstructured enterprisedata.
3. Design hybridsearch architectures combining Vector DBs and Graph DBs (e.g., Azure AISearch, Neo4j) for semantic, contextual, and relationship-based retrieval.
4. Build AgenticAI 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

1. Developand integrate AI-powered chatbots and agents within the Azure ecosystem ,ensuring seamless interoperability with existing platforms and services.
2. IntegrateGenAI solutions with enterprise systems using APIs, event-drivenarchitectures, and message brokers .
3. Buildsecure, scalable backend leveraging Azure App Services, Azure Functions,Bot Framework, Azure Cache for Redis , and related services.
4. Workclosely with Cloud, Digital, Data Engineering, and Business teams todrive adoption and real-world impact.

Production Readiness, MLOps& LLMOps

1. Implement guardrails for safety, hallucination control, data privacy, and responsible AI
2. Ensure enterprise-grade governance, including access control, auditability, and compliance with internal policies
3. 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

1. Strong understanding of LLMs, transformers, embedding, prompt engineering, and evaluation techniques.
2. Experience building end-to-end GenAI/Agentic AI products, including backend services and frontend web apps.
3. Hands-on experience with LangChain, LangGraph, n8n, Co-pilot for building modular, agent-based systems.
4. Practical experience designing multi-agent architectures and orchestrating reasoning and action workflows.
5. Strong experience with Vector Databases and Graph Databases (Azure AI Search, Neo4j, Databricks Vector DB) for hybrid, semantic and relationship-driven search.
6. Proven experience implementing RAG pipelines with structured and unstructured enterprise data.
7. Proficiency in Python, SQL, Spark, and familiarity with additional languages (e.g., JavaScript).
8. Hands-on experience with PyTorch and TensorFlow.
9. Experience working with high-performance, large-scale ML systems in production environments
10. Ability to solve complex problems in language understanding, reasoning, and GenAI system design
11. 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