Data Scientist

About the Role

• 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.
• Possess an incubation mindset: be research-oriented, able to deliver quick POCs, and come up with ideas.
• Demonstrate strong communication and documentation abilities: share knowledge, conduct presentations and sessions, engage with clients and stakeholders, and write good-quality technical documentation.
• Exhibit problem-solving skills with technical depth in addressing real-world problems and an appreciation for business imperatives.
• Commit to continuous learning, including obtaining certifications.

Key Responsibilities

• Design, build, and deploy Hyperscalers-based (Azure, AWS, GCP or Databricks) ML models, including large-scale data processing, computationally intensive statistical modeling, and advanced analytics.
• Implement Generative AI models for business solutions and gain experience with Azure Open AI, AWS Bedrock, Vertex AI, or open-source models.
• Python programming with robust code.
• Develop a framework or generic pipeline to retrieve, optimize, and deploy Open-Source Models.
• Explore and design a reference architecture for developing virtual assistants using foundation models.
• Implement LangChain Frameworks.
• Utilize Linux, HTTP/ REST.
• Work with Cloud Technologies (at least one of GCP, AWS, Azure).
• Use JIRA, Confluence.

Requirements & Tech Stack

• Familiarity with deep learning, machine learning and frameworks (like Keras, TensorFlow or PyTorch etc.), HuggingFace Transformers and libraries (like scikit-learn, spacy, gensim, CoreNLP etc.).
• Understanding of LLMs and Gen AI especially as applied to text and image analysis and training.
• Experience in fine-tuning of LLM models, implementation of AI Guardrails and Responsible AI Framework (explainability, bias detection, hallucination control).

Qualifications

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

Leadership Skills

• People Leadership: Able to lead and mentor a team of 10-15 people.
• Thought Leadership: Papers published in renowned journals, presentations in conferences, patent-filing preferred.

Good to Have

• Linux shell-scripting.
• Multilingual NLP.