Senior Data Scientist - Machine Learning Models

Role Overview :

We are looking for a hands-on Data Scientist with 3+ years of experience in Machine Learning and Deep Learning, along with practical exposure to modern LLM ecosystems. The ideal candidate has strong problem-solving and analytical skills who combines data modeling with engineering rigor and is comfortable building end-to-end AI systems; from experimentation to production deployment.

Key Responsibilities :

- Design, develop, and deploy ML/DL models for real-world business problems.

- Build and optimize deep learning models using frameworks like PyTorch or TensorFlow.

- Work with Large Language Models (LLMs) for tasks such as prompting, fine-tuning.

- Implementing instruction fine-tuning using various techniques and frameworks viz. LoRA, QLoRA, PEFT, Unsloth, etc.

- Develop and manage agentic workflows and LLM-powered pipelines.

- Collaborate with engineering teams to productionize models on cloud platforms.

- Work with structured and unstructured data, including document processing pipelines.

- Ensure scalability, performance, and reliability of deployed models.

Required Skills & Qualifications :

- 3+ years of experience in Machine Learning and Deep Learning.

- Strong proficiency in Python.

- Hands-on experience with PyTorch and/or TensorFlow.

- Solid understanding of ML fundamentals (training, evaluation, optimization, tuning).

- Practical experience with LLMs :

1. Prompt engineering

2. Instruction fine-tuning (PEFT, LoRA, QLoRA, Unsloth)

3. Building agentic workflows

- Experience working with cloud platforms (AWS / GCP / Azure).

Good to Have :

- Experience with Apache Beam or similar big data processing frameworks.

- Exposure to Document AI and Intelligent Document Processing (IDP) systems.

- Experience with OCR pipelines, information extraction, or real estate/financial document workflows.

- Familiarity with MLOps practices (CI/CD, model monitoring, MVC, DVC).

What We Value :

- Ownership mindset with strong execution ability.

- Ability to work across research and engineering boundaries.

- Pragmatic approach to solving business problems using AI.

- Clear communication and collaboration skills.

Outcome Expectations :

- Deliver production-ready ML/LLM solutions.

- Improve model performance and system efficiency over time.

- Contribute to scalable AI architecture and reusable components.