Generative AI Engineers - PAN India

Platlap Solutions; India; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

Platlap Solutions is seeking a Generative AI Engineer (5-7 years) to build, optimize, and productionise cutting-edge AI systems. You will focus on backend orchestration, framework integration, and model performance, turning advanced RAG workflows and multi-agent concepts into production-grade software.

Key responsibilities include AI orchestration, core product integration, and owning the technical lifecycle from prototype to production.

Qualifications

• 2+ years of production RAG pipelines and multi-agent systems using LangChain/LangGraph/LlamaIndex.
• Strong understanding of MCP, A2A protocols, and Agent Developer Kits.
• Proficiency in prompting, fine-tuning workflows, and evaluating LLM outputs.
• Hands-on with Python, pandas, scikit-learn, and PyTorch.
• Experience deploying ML solutions on AWS, Azure, or GCP.
• Knowledge of Docker, Kubernetes, CI/CD, and GitHub Actions.
• Backend coding in Python with basic JS/TS for API integrations.

Responsibilities

• AI Orchestration: Build and optimize bespoke Agentic AI solutions and multi-agent workflows.
• Core Product Integration: Develop pipelines to embed AI models into client environments.
• Technical Delivery: Own lifecycle from rapid prototype to production release.
• Optimization & Debugging: Root-cause analysis, latency and prompt optimizations.
• Best Practices: Implement LLM evaluation, guardrails, and version control.
• Risk Management: Identify model drift and data leakage early.

Skills

RAG workflows

Multi-agent systems

LLM evaluation

Prompting techniques

Python proficiency

Tools

LangChain

LangGraph

LlamaIndex

Model Context Protocols

A2A protocols

PyTorch

Python

Pandas

scikit-learn

Docker

Kubernetes

CI/CD

GitHub Actions

JavaScript/TypeScript

Job description

Generative AI Engineer (5-7 Years)

Role Overview

We are looking for a Generative AI Engineer to build, optimize, and productionise cutting-edge AI systems. You will focus heavily on backend AI orchestration, framework integration, and model performance. In this role, you will take advanced RAG workflows and multi-agent concepts and turn them into scalable, production-grade enterprise software.

Key Responsibilities

• AI Orchestration: Build, test, and optimize bespoke Agentic AI solutions and multi-agent workflows.
• Core Product Integration: Co-develop integration pipelines to embed core AI models into client environments.
• Technical Delivery: Own the technical lifecycle of AI applications from rapid prototype to stable production release.
• Optimization & Debugging: Perform technical debugging, root-cause analysis, and latency/prompt optimizations.
• Best Practices: Implement engineering best practices for LLM evaluation, guardrails, and version control.
• Risk Management: Identify and flag model drift, data leakage, and system risks early.

Technical Requirements

• GenAI Ecosystem: 2+ years of hands-on experience building production RAG pipelines and multi-agent systems using LangChain, LangGraph, or LlamaIndex.
• Protocols & Frameworks: Strong understanding of Model Context Protocols (MCP), A2A protocols, and Agent Developer Kits.
• LLM Engineering: Practical proficiency in prompting techniques, fine-tuning workflows, and evaluating LLM outputs.
• Data Science Stack: Strong hands-on experience leveraging Python, pandas, scikit-learn, and PyTorch.
• Cloud & MLOps: Experience deploying and scaling machine learning solutions on AWS, Azure, or GCP.
• DevOps & Containers: Working knowledge of Docker, Kubernetes, CI/CD pipelines, and GitHub Actions.
• Backend Stack: Production-grade backend coding skills in Python, with basic familiarity in JavaScript/TypeScript for API integrations.