LLM/Agentic AI Engineer

Zorba AI; Kolkata Metropolitan Area; On-site; INR 3,047,000 - 3,649,000; Full time

Job summary

Zorba AI in Kolkata seeks a Lead LLM / Agentic AI Engineer to drive an AI-led procurement transformation. You will design an end-to-end solution blending classical ML and GenAI to deliver actionable procurement insights.

The role requires hands-on experience building production-grade LLMs, multi-agent orchestration, real-time data integration, and ERP system integration. Onsite work with client-facing engagement; travel may be required.

Qualifications

• 8+ years of software/AI engineering experience.
• Hands-on experience with LLMs and Generative AI.
• Production-grade GenAI applications experience.
• Knowledge of RAG architectures and embeddings.

Responsibilities

• Lead architecture and development of enterprise-grade LLM/GenAI for procurement transformation.
• Design and implement Agentic AI solutions with multi-step workflows.
• Build and orchestrate multiple AI agents for procurement activities.
• Design multi-agent workflows with tools, APIs, data sources and enterprise systems.
• Build a Procurement Category Insights Engine combining ML and LLM capabilities.
• Integrate real-time and external data sources to enrich insights.
• Integrate the AI solution with ERP and enterprise systems.
• Develop tool/API integrations for AI agents to retrieve information and act.
• Create end-to-end workflows with a simplified user experience.
• Implement RAG-based solutions for enterprise knowledge context.
• Define prompt engineering strategies and guardrails.
• Collaborate with Data Scientists to include classical ML models.
• Work with backend/frontend engineers for a complete product.
• Ensure scalability, security, and production readiness.
• Lead client discussions and handover deliverables.

Skills

LLM / GenAI

Production-grade GenAI

RAG architectures

Embeddings / vector DBs

Multi-Agent Orchestration

LangChain / LangGraph

REST/API integration

Python

AWS architectures

ERP / enterprise integration

Tools

ERP integrations

APIs

Job description

Job Title

Lead LLM / Agentic AI Engineer – AI-Led Procurement Transformation

Location: Kolkata

Experience: 8+ Years

Project Duration: Approximately 6 Months

Budget: Up to ₹3 Lakh/month

Work Mode: Onsite / Client-facing

Travel: May be required

Cloud: AWS preferred/important

Technology Focus: 50% Classical ML / Statistical Modelling + 50% LLM / GenAI

Role Overview

We are looking for a highly experienced Lead LLM / Agentic AI Engineer to drive an AI-led transformation initiative for the Procurement function. The objective is to build a Procurement Category Insights Engine that combines classical Machine Learning, statistical modelling and Generative AI to generate actionable business insights. The ideal candidate must have strong hands‑on experience building production‑grade LLM and Agentic AI solutions, including Multi‑Agent Orchestration, real‑time data integration, external data sources and enterprise/ERP integrations. This is not a basic chatbot or RAG development role. The candidate will be expected to design and build a proper end‑to‑end Agentic AI solution capable of generating insights and triggering actionable outcomes through a simplified user experience.

Key Responsibilities

• Lead the architecture and development of an enterprise‑grade LLM/GenAI solution for procurement transformation.


• Design and implement Agentic AI solutions capable of reasoning, planning and executing multi‑step business tasks.


• Build and orchestrate multiple AI agents to perform specialized procurement‑related activities.


• Design Multi‑Agent workflows and determine when agents should interact with tools, APIs, data sources and enterprise systems.


• Build a Procurement Category Insights Engine combining ML, statistical modelling and LLM capabilities.


• Integrate real‑time and external data sources to enrich procurement insights.


• Integrate the AI solution with ERP and enterprise systems.


• Develop tool/API integrations that allow AI agents to retrieve information and perform approved business actions.


• Build actionable workflows where users can initiate business processes through a single‑button / simplified action experience.


• Develop RAG‑based solutions where required for enterprise knowledge and contextual understanding.


• Design prompt engineering strategies, agent instructions, evaluation mechanisms and AI guardrails.


• Implement LLM evaluation, monitoring and performance optimization.


• Collaborate with Data Scientists to incorporate classical ML and statistical models into the overall AI solution.


• Work with backend and frontend engineers to deliver a complete end‑to‑end application.


• Ensure solutions are scalable, secure, reliable and production‑ready.


• Rapidly adapt the architecture and solution as business problem statements evolve.


• Lead technical discussions with client stakeholders and explain AI architecture and solution capabilities.


• Support testing, production deployment, documentation and final client handover.

Required Technical SkillsGenerative AI / LLM

• 8+ years of overall software/AI engineering experience.


• Strong hands‑on experience with LLMs and Generative AI.


• Experience building production‑grade GenAI applications.


• Strong understanding of LLM architecture, prompting and model selection.


• Experience with commercial and/or open‑source LLMs.


• Strong experience with RAG architectures.


• Experience with embeddings and vector databases.

Agentic AI

• Strong hands‑on experience building Agentic AI solutions.


• Experience with Multi‑Agent Orchestration.


• Experience with frameworks such as LangGraph, LangChain, AutoGen, CrewAI or equivalent.


• Experience designing agent workflows, state management and tool calling.


• Ability to build agents that interact with APIs, databases and enterprise systems.


• Experience implementing agent guardrails and evaluation mechanisms.

Enterprise Integration

• Experience integrating AI solutions with ERP systems and enterprise applications.


• Strong REST/API integration experience.


• Experience consuming external and real‑time data sources.


• Experience building tool/API layers for AI agents.


• Understanding of enterprise security and access controls.

Machine Learning / Data Science

• Strong foundation in classical Machine Learning.


• Strong understanding of statistics and statistical modelling.


• Experience integrating predictive/ML models with GenAI applications.


• Experience with Python and common ML libraries.

Cloud & Engineering

• Strong Python programming skills.


• Strong software engineering and system‑design fundamentals.


• Experience with AWS/cloud‑based AI architectures.


• Experience with APIs, microservices and scalable backend systems.


• Understanding of CI/CD, containerization and production deployment.

Good to Have

• Procurement / Source‑to‑Pay / Supply Chain domain experience.


• Experience developing procurement analytics or category intelligence solutions.


• MCP / Model Context Protocol experience.


• Real‑time event/data processing.


• AWS Bedrock or similar managed GenAI services.


• Kafka or other streaming technologies.


• LLM observability and evaluation platforms.


• Experience with enterprise ERP platforms such as SAP, Oracle or similar.


• Experience working in consulting or client‑facing transformation projects.

Ideal Candidate

The ideal candidate should be a hands‑on senior AI engineer/architect, not simply a prompt engineer or RAG developer.

The Candidate Should Be Capable Of

Business Problem - AI Architecture - Multi-Agent Solution - Enterprise Integration - Production Deployment - Client Handover

Technology Balance

50% - Classical ML / Modelling / Statistical Analytics

50% - LLM / Generative AI / Agentic AI

The LLM/Agentic AI component is particularly important for this role.

Candidates NOT to Prioritize

Do Not Prioritize Candidates Who Have Only

• Basic ChatGPT/API integration experience.


• Simple RAG chatbot experience.


• Prompt engineering without software engineering.


• No Multi‑Agent experience.


• No production GenAI implementation experience.


• No enterprise/API integration experience.


• Pure Data Science experience without hands‑on GenAI engineering.

Skills: ml,data,procurement,enterprise