<p><p><b>About the Role:</b><br/><br/></p><p>We are looking for a highly experienced GenAI Technical Architect with strong domain expertise in Generative AI, Transformer architectures, and LLM-based solution implementation.
The ideal candidate will lead technical programs and architect cutting-edge solutions using large language models (LLMs), Retrieval-Augmented Generation (RAG), and related GenAI technologies.
This is a hybrid role combining deep hands-on knowledge, architecture-level thinking, and program management skills.<br/><br/><b>Key Responsibilities :</b><br/><br/></p><p>- Architect and lead the end-to-end implementation of GenAI solutions, including LLM selection, RAG integration, knowledge retrieval, and model deployment.<br/><br/></p><p>- Design and implement systems using Transformer architectures (Encoder/Decoder models), leveraging frameworks like Hugging Face, LangChain, or custom pipelines.<br/><br/></p><p>- Develop, evaluate, and deploy models using both Autoencoder (BERT, RoBERTa, DistilBERT) and Autoregressive (GPT, LLaMA, Mistral, PaLM, BLOOM, Claude, CodeGen, OPT) paradigms.<br/><br/></p><p>- Implement RAG (Retrieval-Augmented Generation) architecture with real-world datasets and search systems.<br/><br/></p><p>- Lead LLM fine-tuning and prompt engineering for domain-specific use cases and optimized performance.<br/><br/></p><p>- Utilize LangChain or similar frameworks to build intelligent pipelines and agents that interact with data and APIs.<br/><br/></p><p>- Drive the design and implementation of Knowledge Graphs, integrating structured and unstructured data for enterprise knowledge systems.<br/><br/></p><p>- Build and execute LLM evaluation pipelines using standard evaluation metrics like RAGAS, ROUGE, BLEU, BERTScore, etc.<br/><br/></p><p>- Collaborate with cross-functional teams including data science, product management, software engineering, and stakeholders to align technical roadmaps with business objectives.<br/><br/></p><p>- Mentor junior engineers and data scientists on GenAI best practices and tooling.<br/><br/></p><p>- Stay up-to-date with the latest advancements in Generative AI, NLP, and ML research and incorporate them into the companys AI strategy.<br/><br/><b>Required Technical Skillset :</b><br/><br/></p><p>- Strong expertise in Transformer architectures, including both:<br/><br/></p><p>- Autoencoding models: BERT, RoBERTa, DistilBERT<br/><br/></p><p>- Autoregressive models: GPT (OpenAI, GPT-J), LLaMA, Claude, Mistral, PaLM, CodeGen, BLOOM, OPT, etc.<br/><br/></p><p>- Solid understanding of the differences between Autoencoder and Autoregressive models and their use cases.<br/><br/></p><p>- Proven experience building and deploying RAG-based systems (e.g., using FAISS, ElasticSearch, or vector DBs like Pinecone, Weaviate).<br/><br/></p><p>- Proficiency with LangChain for orchestrating LLM applications.<br/><br/></p><p>- Demonstrated experience in fine-tuning LLMs for custom datasets or tasks.<br/><br/></p><p>- Strong understanding of ML Ops and GenAI Ops pipelines, from experimentation to deployment.<br/><br/></p><p>- Experience in AI/ML development lifecycle, including data preparation, model training, evaluation, and monitoring.<br/><br/></p><p>- Exposure to Knowledge Graph design and implementation is highly desirable.<br/><br/></p><p>- Familiarity with evaluation frameworks and scoring metrics: RAGAS, ROUGE, BLEU, BERTScore, etc.<br/><br/></p><p>- Programming expertise in Python and hands-on experience with Hugging Face Transformers, OpenAI APIs, LangChain, PyTorch/TensorFlow, LLM fine-tuning libraries.<br/><br/></p><p>- Knowledge of cloud-based AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and containerization (Docker, Kubernetes).</p><br/></p> (ref:hirist.tech)