GenAI Data Scientist

Key Responsibilities

• Model Development: Research, design, and develop state-of-the-art generative models such as GPT, GANs, VAEs, or diffusion models for tasks like text generation, summarization, reasoning, Q&A, or predictive analytics.
• Fine-Tuning: Fine-tune pre-trained models (e.g., Llama3, Mistral, OpenAI GPT, BERT, T5) to meet domain-specific requirements.
• Data Preparation: Clean, preprocess, and structure large datasets to train, validate, and test generative AI models.
• Deployment: Implement scalable solutions for deploying generative AI models in production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
• Collaboration: Work closely with cross-functional teams, including product managers, engineers, and stakeholders, to align AI solutions with business goals.
• Evaluation: Develop metrics and benchmarks to evaluate model performance and ensure quality outputs.
• Research: Stay up to date with the latest advancements in AI/ML, particularly in generative AI techniques, and propose innovative solutions.
• Ethical AI Practices: Ensure ethical considerations are addressed in model development, including fairness, accountability, and explainability. Experience of architecting AI systems to solve complex business problems.
• Build advanced RAG pipelines, text chunking, and retrieval, LLM Prompt Engineering, using Vector Databases. Implement right LLM selection based on use cases and client criteria (GPT-4, Llama2, Mistral, Claude, Gemini, Flan, BERT) while managing trifecta of accuracy, cost, and latency/scale.
• Develop, fine-tune, context tune and implement state-of-the-art NLP models including Large Language Models like GPT, Llama3.1, Claude, BLOOM, Flan-T5, Falcon etc. fine-tuning PEFT LoRa/QLoRa adapters
• design AI systems & architectures, while considering good Responsible AI standards and AI Governance.
• Build Agentic AI, AutoGen, Muti-agents use cases, AI Autonomous Agents and LLM orchestration architectures for enterprise data at scale in Python.
• Hands on experience with complementary technologies around LLMs like embedders, vector databases (chroma, weaviate etc.), orchestration tools like Langchain, LlamaIndex etc.
• Conduct research and experimentation to improve existing models and propose novel approaches.
• Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications.
• Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities.
• Document research findings, prepare technical reports, and contribute to whitepaper/scientific publications.
• Provide deep leadership and coaching in the project delivery lifecycle. Focus on shared learning, continuous improvement, and drive adoption of best practices.

Key skills:

• GenAI
• NLP
• LLM
• Natural Language Processing