About the Role :

We are seeking an experienced Generative AI Technical Architect to lead the design, development, and deployment of advanced GenAI solutions across multiple domains.

This role is ideal for professionals with deep expertise in transformer-based architectures, large language models (LLMs), Retrieval-Augmented Generation (RAG), and modern AI/ML practices.

You will collaborate closely with cross-functional teams, including data scientists, machine learning engineers, product managers, and enterprise architects, to deliver scalable AI-first solutions.

Key Responsibilities :

- Design and architect end-to-end GenAI solutions leveraging transformer-based models.

- Lead the implementation of encoder-only (e.g, BERT, RoBERTa) and decoder-only (e.g, GPT, LLaMA) models for various enterprise use cases.

- Define architecture for Retrieval-Augmented Generation (RAG) systems, including vector store integration and retriever-reader pipelines.

- Fine-tune and optimize pre-trained LLMs using custom datasets.

- Compare and evaluate models across encoder-decoder paradigms (Autoencoder vs. Autoregressive).

- Implement prompt engineering and embedding-based retrieval techniques.

- Work with model families such as GPT, BLOOM, Mistral, Claude, CodeGen, OPT, PaLM, and others.

- Leverage LangChain or LLamaIndex for building modular GenAI applications.

- Implement pipelines for training, fine-tuning, and evaluating models using Hugging Face Transformers, PEFT, or LoRA.

- Utilize vector databases like FAISS, Pinecone, Weaviate, or Chroma for document retrieval.

- Architect and implement knowledge graphs, entity linking, and semantic search for enterprise knowledge management.

- Design robust evaluation frameworks using RAGAS, ROUGE, BLEU, BERTScore, and custom metrics to assess performance, relevance, and hallucination rates.

- Guide and mentor junior engineers and researchers in GenAI practices.

- Collaborate with stakeholders to align technical solutions with business objectives.

Required Skills & Qualifications :

- Strong experience (8+ years) in AI/ML, with a significant focus on NLP and Generative AI.

- Deep understanding of transformer architectures: encoder-only (BERT-like), decoder-only (GPT-like), and encoder-decoder (T5-like).

- Hands-on expertise in RAG-based architectures and end-to-end implementation.

- Practical experience in fine-tuning and deploying LLMs in production.

- Proficient in Python, PyTorch/TensorFlow, and tools like Hugging Face, LangChain, and Vector DBs.

- Experience with prompt tuning, PEFT/LoRA, and scalable inference.

- Familiarity with evaluation frameworks for generative models.

- Prior experience with knowledge graph development is a strong plus.

- Strong communication and leadership skills