Senior Data Scientist - LLM Models (5-9 yrs)

Job Description :

1. Collaborate with cross-functional teams of engineers, data scientists, product managers, and technical program managers to design and deploy AI-driven solutions for data extraction and analysis.

2. Develop and optimize AI software components, focusing on LLMs for document understanding, semantic analysis, and structured and unstructured data extraction.

3. Implement techniques for large-scale LLM inference, fine-tuning, similarity search, and model observability to meet enterprise-level scalability and performance needs.

4. Leverage advanced tools and platforms like AWS Textract, Hugging Face, VectorDBs, PyTorch, and OpenAI APIs to build state-of-the-art data extraction systems.

5. Introduce and refine LLM optimization techniques to enhance cost-efficiency, latency, and throughput in production.

6. Drive experimentation, governance, and performance monitoring frameworks for AI-powered data extraction pipelines.

7. Shape the technical vision and long-term strategy for AI-driven document processing systems.

8. Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production

Minimum Qualifications :

1. Bachelor's degree in computer science, Engineering, AI, or a related field, plus at least 4 years of experience developing AI/ML technologies.

2. At least 5 years of programming experience with Python, Go, Scala, or Java.

3. 4+ years of experience deploying scalable AI systems on cloud platforms (e.g., AWS, Google Cloud, Azure).

4. Proven expertise in NLP and computer vision techniques to complement LLM-based data extraction.

5. Strong hands-on experience with statistical packages and ML libraries (e.g. Python scikit learn, Spark MLlib, etc.)

6. Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost

7. Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers