Machine Learning Engineer

Dear All,

Machine Learning Engineer

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.

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.