Generative AI Engineer - NLP/RAG

Description :

This role requires deep technical expertise in modern AI architectures, hands-on experience with foundation models, and the ability to build high-performance AI systems leveraging GPU infrastructure.

Key Responsibilities :

- Design and implement advanced NLP, Deep Learning, and Generative AI models for enterprise applications.

- Work with Transformer-based architectures across language and vision domains.

- Develop solutions using embedding models, semantic search, and vector-based retrieval systems.

- Architect and implement Retrieval-Augmented Generation (RAG) pipelines, including

advanced techniques such as GMM and RAPTOR.

- Apply advanced prompt engineering strategies including Chain-of-Thought, Tree-of-Thoughts, and Graph-of-Thoughts methodologies.

- Build and manage AI agents and orchestration frameworks such as LangChain, LangGraph, and DeepAgents.

- Utilize SDKs and agent frameworks including AutoGen and ADK for building autonomous AI workflows.

- Fine-tune and optimize foundation models (LLMs/SLMs) for domain-specific use cases.

- Optimize AI inference performance using technologies such as TensorRT, vLLM, and GPU acceleration.

- Work with high-performance GPUs such as H100, A100, L40S, and RTX 6000.

- Collaborate with business stakeholders to translate requirements into scalable AI solutions.

- Support pre-sales initiatives, including technical proposal creation, solution architecture, and RFP responses.

- Mentor engineering teams and contribute to building or expanding an AI/GenAI Center of Excellence (CoE).

Required Skills & Expertise :

- Strong expertise in NLP, Deep Learning, and Generative AI.

- Hands-on experience with Transformer architectures and modern deep learning frameworks.

- Proficiency in embedding models, vector databases, and semantic search techniques.

- Strong understanding of data structures such as trees and graphs and their applications in AI systems.

- Knowledge of CUDA architecture and parallel processing concepts.

- Experience with foundation models such as BERT, GPT, T5, and similar architectures.

- Advanced knowledge of prompt engineering techniques and reasoning frameworks.

- Experience building RAG-based AI applications and scalable data pipelines.

- Experience with AI agent frameworks and orchestration tools.

- Hands-on experience with AI inference optimization and GPU-based infrastructure.

- Ability to lead technical teams and drive innovation in AI solution development.

Preferred Qualifications :

Transformation initiatives.

- Prior experience supporting pre-sales engagements, technical proposals, or solution architecture discussions.

- Candidates who have built their careers in AI/ML from early stages will be strongly preferred.

- NVIDIA certifications or similar AI infrastructure certifications are a plus.

Educational Qualification :

Intelligence, or a related field.