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 secure 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.

📌 Generative AI Engineers (India)
🏢 Platlap Solutions
📍 India