Job Description:
AI Lead Engineer

Role Overview

We are seeking a Lead Generative AI Engineer with robust foundations in deep learning, transformer architecture, and practical experience building GenAI applications beyond basic RAG systems. The ideal candidate has hands-on experience/technical familiarity with LLM fine-tuning, multimodal models, retrieval systems, agentic frameworks, retrieval architectures, and production-grade ML deployment.

This role will partner with engineering, data science, and CX teams to build intelligent agents, multimodal experiences, personalization systems, and knowledge-grounded AI solutions that power the future of customer engagement for global brands.

Key Responsibilities
Generative AI, Multimodal Systems & Agentic Frameworks
Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar.
Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations.




Develop Knowledge Graph (KG)-assisted AI systems, including entity extraction, linking, and KG-augmented retrieval.
Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails.

Deployment, APIs & Cloud Engineering
Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker.
Deploy and monitor ML/AI systems in AWS/Azure/GCP, optimizing for cost, latency, and reliability.
Collaborate with MLOps teams on CI/CD pipelines, model versioning, monitoring, and automated evaluation.
Work with big data technologies including Apache Spark, Hadoop, and NoSQL databases such as MongoDB.

Model Development & Applied AI Engineering
Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g., PyTorch, TensorFlow).
Implement fine-tuning, alignment (RLHF/RLAIF), LoRA/QLoRA, pruning, and model evaluation pipelines.
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