Machine Learning Engineer

7 days ago


madhya pradesh, India Startech Software Pvt Ltd Full time

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 PEFT approaches. 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 vision concepts. 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. Industry Software Development Employment Type Full-time



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