AI Engineer

techcarrot; Hyderabad City Taluka; On-site; PKR 2,400,000 - 4,200,000; Full time

Job summary

techcarrot is seeking a highly skilled AI Engineer to design and build enterprise-grade, production-ready GenAI systems that enhance how users interact with enterprise products, services, insights, and recommendations.

You will architect multi-agent, tool-augmented GenAI solutions with reasoning, planning, and contextual retrieval across enterprise data sources, ensuring secure, scalable, and governed deployments with reliability and observability in production environments.

Qualifications

• Strong understanding of LLMs, transformers, embeddings, prompt engineering, and evaluation.
• Experience building end-to-end GenAI/Agentic AI products including backend and frontend apps.
• Hands-on experience with LangChain, LangGraph, n8n, Co-pilot.
• Design multi-agent architectures and orchestration of reasoning and actions.
• Experience with Vector and Graph Databases (Azure AI Search, Neo4j, Databricks Vector DB).
• Proficiency in Python, SQL, Spark; familiarity with JavaScript.
• Hands-on with PyTorch and TensorFlow.
• Experience deploying GenAI solutions on Azure (ADF, Databricks, Azure AI Search, Databricks Genie).

Responsibilities

• Architect and deliver production-grade GenAI solutions.
• Implement Retrieval-Augmented Generation (RAG) pipelines over enterprise data.
• Design hybrid search architectures for semantic, contextual, and relationship retrieval.
• Build agentic AI workflows with multi-agent orchestration and tool-calling.
• Ensure secure, scalable, governed GenAI systems with reliability and observability.
• Collaborate with Cloud, Data, and Business teams to drive real-world impact.

Skills

LLMs / Transformers

GenAI / Agentic AI products

Backend & frontend apps

LangChain / LangGraph / n8n

Multi-agent architectures

Vector/Graph databases

RAG pipelines

Python / SQL / Spark

PyTorch / TensorFlow

Azure deployment

Tools

LangChain

LangGraph

n8n

Co-pilot

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 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 & 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