APPLIED AI SCIENTIST

The Applied AI Scientist will lead the design, tuning, and evaluation of GenAI solutions. This role focuses on creating high-quality, business-relevant AI models, including RAG pipelines, LLM integrations, and prompt optimization strategies.

Key Duties/ Responsibilities

• Design, fine-tune, and evaluate large language model (LLM) pipelines for internal tools and customer-facing use cases.
• Lead prompt engineering experiments to improve output quality and reduce hallucination.
• Establish model evaluation frameworks using tools like Promptfoo, Trulens, LangSmith.
• Collaborate with Enablement and Systems Engineers to integrate AI models into reusable components.
• Contribute to innovation showcases and internal learning sessions.
• Design, develop, and evaluate traditional ML models (e.g., classification, regression, clustering) for use cases such as resume matching, anomaly detection, or recommendation systems.
• Perform feature engineering, preprocessing, and model training on structured datasets using tools like scikit-learn, XGBoost, or TensorFlow.
• Collaborate with data and engineering teams to operationalize ML models via APIs or batch workflows.

Leadership Skills:

• Strong analytical thinking with the ability to guide AI solution design.
• Able to mentor engineers on GenAI concepts and best practices.
• Collaboration across engineering and innovation teams.

Required Technical Skills:

• Proficiency with HuggingFace Transformers, LangChain, LlamaIndex, RAG architectures.
• Experience with Python, PyTorch, OpenAI API, and model evaluation tools.
• Familiarity with vector databases (e.g., FAISS, Weaviate, Pinecone or equivalent vector store).
• Prompt evaluation using LangSmith, Promptfoo, or similar frameworks.

Qualification:

• Bachelor s or Master s degree in Computer Science, AI/ML, or related field.
• Minimum 6 years of industry experience with at least 2 years hands-on with GenAI applications in a production or product-context environment.

Experience

• 6 + Years