Job Title

Dear All,We are seeking a highly capable Machine Learning Engineer with deep expertise in fine-tuning Large Language Models (LLMs) and Vision-Language Models (VLMs) for intelligent document processing. This role requires strong knowledge of PEFT techniques (LoRA, QLoRA), quantization, transformer architectures, prompt engineering, and orchestration frameworks like LangChain. You’ll work on building and scaling end-to-end document processing workflows using both open-source and commercial models (OpenAI, Google, etc.), with an emphasis on performance, reliability, and observability.Key Responsibilities:- Fine-tune and optimize open-source and commercial LLMs/VLMs (e.g., LLaMA,Cohere, Gemini, GPT-4) for structured and unstructured document processing tasks. - Apply advanced PEFT techniques (LoRA, QLoRA) and model quantization to enable efficient deployment and experimentation. - Design LLM-based document intelligence pipelines for tasks like OCR extraction, entity recognition, key-value pairing, summarization, and layout understanding. - Develop and manage prompting techniques (zero-shot, few-shot, chain-of-thought, self-consistency) tailored to document use-cases. - Implement LangChain-based workflows integrating tools, agents, and vector stores for RAG-style processing. - Monitor experiments and production models using Weights & Biases (W&B) or similar ML observability tools. - Work with OpenAI (GPT series), Google PaLM / Gemini, and other LLM/VLM APIs for hybrid system design. - Collaborate with cross-functional teams to deliver scalable, production-ready ML systems and continuously improve model performance. - Build reusable, well-documented code and maintain a high standard of reproducibility and traceability. - Performance evaluation of LLM and vLLM models for optimizing accuracy, latency.Required Skills & Experience:- Hands-on experience with transformer architectures and libraries like HuggingFace Transformers. - Deep knowledge of fine-tuning strategies for large models, including LoRA, QLoRA, and other PEFTapproaches. - Experience in prompt engineering and developing advanced prompting strategies. - Familiarity with LangChain, vector databases (e.g., FAISS, Pinecone), and tool/agent orchestration. - Strong applied knowledge of OpenAI, Google (Gemini/PaLM), and other foundational LLM/VLM APIs. - Proficiency in model training, tracking, and monitoring using tools like Weights & Biases (W&B). - Solid understanding of deep learning, machine learning, natural language processing, and computer visionconcepts. - Experience working with document AI models (e.g., LayoutLM, Donut, Pix2Struct) and OCR tools (Tesseract, EasyOCR, etc.). - Proficient in Python, PyTorch, and related ML tooling.Nice-to-Have:- Experience with multi-modal architectures for document + image/text processing. - Knowledge of RAG systems, embedding models, and custom vector store integrations. - Experience in deploying ML models via FastAPI, Triton, or similar frameworks. - Contributions to open-source AI tools or model repositories. - Exposure to MLOps, CI/CD pipelines, and data versioning.Qualifications:- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.Why Join Us?- Work on cutting-edge GenAI and Document AI use-cases. - Collaborate in a fast-paced, research-driven environment. - Flexible work arrangements and growth-focused culture. - Opportunity to shape real-world applications of LLMs and VLMs.IndustrySoftware DevelopmentEmployment TypeFull-time