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Role Overview
We are seeking a Lead Generative AI Engineer with strong 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 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.
· Develop information retrieval systems, including hybrid dense–sparse retrieval, ranking, knowledge graphs, and relevance optimization.
· Build predictive models and ML pipelines from scratch, including data preparation, feature engineering, and model selection.
Required Technical Skills
· Programming: Python (advanced), SQL; robust experience with API development and data engineering,
· Backend Frameworks: Flask, FASTAPI, Django
· Machine Learning: Predictive modelling, deep learning, optimization, embeddings, vector search, model evaluation.
· Generative AI: LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs.
· Cloud Platforms: AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems.
· Data Technologies: Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing.
· Math Foundations: Linear algebra, probability, statistics.
Experience Requirements
· Minimum 5-6 years of hands-on software development experience including building and deploying machine learning models into production.
· 2+ years of experience working with deep learning, GenAI, or transformer-based architectures.
· Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning).
· Experience integrating AI systems in enterprise-grade environments.
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