Job
Description
Responsibilities: • Design and develop GenAI-based solutions using LLMs (e.g., Bedrock, OpenAI, Claude) for text, image, table, diagram, and multi-modal applications. • Implement a multi-agent system that integrates structured and unstructured data sources, including knowledge graphs, embeddings, and vector databases. • Build and deploy agentic AI workflows capable of autonomous task completion, using frameworks like LangChain, LangGraph, or CrewAI. • Perform fine-tuning, retraining, or adaptation of open-source or proprietary LLMs for specific domain tasks. • Collaborate with data scientists and domain experts to curate and preprocess training datasets. • Integrate models with scalable backend APIs or pipelines (REST, FastAPI, gRPC) for real-world applications. • Stay updated with state-of-the-art research and actively contribute to enhancing model performance and interpretability. • Optimize inference, model serving, and memory management for deployment at scale. Qualifications: • Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Data Science, or related field. • 5+ years of hands-on experience in Deep Learning, NLP, and LLMs. • Proven experience with at least one end-to-end project involving multi-modal RAG and Agentic AI. • Proficient in Python and ML/DL libraries such as PyTorch, TensorFlow, Transformers (HuggingFace), LangChain, LangGraph, Bedrock, or similar • Experience in fine-tuning or adapting LLMs (using LoRA, QLoRA, PEFT, or full fine-tuning). • Experience in building a multi-agent system. • Strong understanding of knowledge graphs, embeddings, vector databases (e.g., FAISS, Chroma, Weaviate), and prompt engineering. • Strong understanding and experience of a cloud platform like AWS. • Familiarity with containerization (Docker, Kubernetes) Preferred Skills • Experience in the Biopharma industry. • Design and implement user-friendly interfaces for AI applications. • Utilize modern web frameworks (e.g., React, Vue.js) to create engaging user experiences. • Develop scalable and efficient backend systems to support the deployment of AI models. • Integrate with cloud platforms (AWS) for infrastructure management. • Hands-on experience in vision-language models (e.g., CLIP, BLIP, LLaVA). • Publications, Kaggle competitions, or GitHub projects in GenAI Show more Show less
