Data Scientist

About the Role

This role focuses on inventing, researching, envisioning, designing, and delivering AI/ML applications at scale that provide measurable value.

Key Responsibilities

• Invent, research, envision, design, solution, and deliver AI/ML applications at scale delivering measurable value.
• Build end-to-end AI/ML solutions: Develop, refine, implement, customize, and deploy state-of-the-art Machine Learning algorithms and systems, and AI models.
• Design, develop, and research Machine Learning systems, models, and schemas through data science and data analytics prototypes.
• Continuous learning, including certifications.
• Foster an incubation mindset: Be research-oriented, capable of delivering quick POCs, and generating new ideas.
• Communication and Documentation: Share knowledge, conduct presentations and sessions, engage with clients and stakeholders, and write high-quality technical documentation.
• Problem Solving: Demonstrate technical depth in solving real-world problems with an understanding of business imperatives.

Requirements & Tech Stack

• Hyperscalers-based ML models: Design, build, and deploy models on Azure, AWS, GCP, or Databricks, including large-scale data processing, computationally intensive statistical modeling, and advanced analytics.
• Generative AI models: Implement Generative AI models for business solutions with experience on Azure Open AI, AWS Bedrock, Vertex AI, or open-source models.
• Python programming: Proficient with robust code.
• Open-Source Model Deployment: Develop a framework or generic pipeline to retrieve, optimize, and deploy Open-Source Models.
• Virtual Assistant Architecture: Explore and design a reference architecture for developing virtual assistants using foundation models.
• Deep Learning & Machine Learning Frameworks: Familiarity with frameworks such as Keras, TensorFlow, or PyTorch, and libraries like HuggingFace Transformers, scikit-learn, spacy, gensim, CoreNLP.
• LLMs and Gen AI: Understanding of LLMs and Gen AI, especially as applied to text and image analysis and training.
• LLM Fine-tuning & Responsible AI: Experience in fine-tuning of LLM models, implementation of AI Guardrails and Responsible AI Framework (explainability, bias detection, hallucination control).
• LangChain Frameworks: Experience in implementing LangChain Frameworks.
• System Technologies: Familiarity with Linux, HTTP/REST.
• Cloud Technologies: Experience with at least one of GCP, AWS, or Azure.
• Project Management Tools: Familiarity with JIRA, Confluence.

Qualifications

Working knowledge is required for:

• Generative AI models (Azure Open AI, AWS Bedrock, Vertex AI, open-source)
• Python programming
• Open-Source Model Deployment framework development
• Virtual Assistant architecture design
• Deep learning, machine learning, and frameworks (Keras, TensorFlow, PyTorch)
• HuggingFace Transformers and libraries (scikit-learn, spacy, gensim, CoreNLP)
• LLMs and Gen AI for text and image analysis/training
• LLM fine-tuning, AI Guardrails, and Responsible AI Framework
• LangChain Frameworks implementation
• Linux, HTTP/REST
• Cloud Technologies (GCP, AWS, Azure)
• JIRA, Confluence

Good to Have Skills

• Linux shell-scripting
• Multilingual NLP

Soft Skills

• Good communication.
• Ability to work in a team as well as independently deliver modules.
• Team player.

Leadership Skills

• People Leadership: Ability to lead and mentor a team of 10-15 people.
• Thought Leadership: Preferred experience with papers published in renowned journals, presentations in conferences, and patent-filing.