Data Scientist

Job Description:

- The role is an intersection of AIML, Generative AI and Technology. You will be required to develop key integrations with RAG, develop modules and solutions for Agentic AI Platform as well as train/fine-tune models on proprietary data. While the role would be a combination of AI and Tech - it is highly weighed toward AI.

- You will be involved in developing innovative solutions to complex business problems. You will collaborate closely with cross-functional teams to extract actionable insights from large and diverse datasets, enabling the company to make informed strategic decisions.

- Develop and implement advanced statistical, ML/AI, and LLM models to solve complex business problems and optimize performance.

- Fine-tune and train domain-specific LLMs, including multi-modal (text, speech, vision, image) models.

- Design and implement RAG systems and GenAI architectures (e.g., Knowledge Graphs, modular agent architectures) for enhanced agent outcomes.

- Finetune, Train & Deploy LLM Models, Vision Models, Voice Models

- Should be able to manage Model Ops & Deployment

- Develop Modules for Agents like Guardrails, Caching, Model Compression, Evaluations etc.

- Read, interpret, and implement complex academic research, adapting it into scalable, enterprise-ready AI systems.

- Support in developing end-to-end data science pipelines: data collection, cleaning, feature engineering, model development, validation, deployment.

- Apply MLOps/LLMOps practices for model lifecycle management and continuous improvement.

Required Qualifications:

- Master's or Ph.D. degree in a quantitative field such as Computer Science, Statistics, Mathematics, or related discipline. (Candidates with exceptional industry experience may be considered without advanced degrees.

- Proficiency in programming languages such as Python or R, and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn).

- Strong knowledge in a wide range of statistical modeling techniques (e.g., regression, clustering, time series analysis, Bayesian inference, NLP, LLM Fine-Tuning) and machine learning algorithms (e.g., decision trees, random forests, neural networks).

- Strong communication and interpersonal skills, with the ability to effectively present complex findings to both technical and non-technical audiences.