Data Scientist_5

Fractal Analytics•California, CA

7d•$150,000 - $170,000

About The Position

Fractal Analytics is seeking a GenAI Data Scientist with hands-on expertise in building production-grade Large Language Model (LLM)-powered applications such as agentic chatbots, semantic search engines, and contextual assistants. The ideal candidate will be deeply technical with a strong foundation in LLM architecture and fine-tuning, strong understanding of Foundation Model capabilities, RAG design and performance evaluation frameworks. This role is central to driving the development of innovative generative AI experiences that empower users and transform enterprise decision-making.

Requirements

Strong experience working with LLMs (e.g., GPT-4, LLaMA, Claude, PaLM) and frameworks such as Hugging Face, LangChain, LlamaIndex, or Haystack.
Experience implementing agent-based architectures for autonomous task execution.
Solid grounding in NLP techniques including embeddings, vector databases, text classification, summarization, and QA systems.
Proficiency in Python and ML libraries like PyTorch, TensorFlow, scikit-learn.
Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Familiarity with MLOps practices and tools for model deployment and monitoring.
Ability to understand user pain points and propose intuitive AI solutions.
Strong problem-solving and communication skills to work across technical and business stakeholders.
Proven track record of delivering scalable GenAI solutions in enterprise environments.

Nice To Haves

Master’s/PhD in Computer Science, AI, Data Science, or a related field.
Experience deploying GenAI solutions in a B2B enterprise or consulting environment.
Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma) and hybrid search strategies.

Responsibilities

Design and develop LLM-powered applications such as agentic chatbots, smart search, contextual recommendation systems, and document summarizers.
Fine-tune open-source and proprietary foundation models (e.g., GPT, LLaMA, Claude) for domain-specific tasks using best practices.
Implement Retrieval-Augmented Generation (RAG) frameworks for enterprise-grade knowledge access.
Integrate AI assistants with internal systems and APIs for multi-step reasoning and tool usage.
Evaluate emerging GenAI tools and frameworks and incorporate them into scalable architectures.
Experiment with techniques like few-shot learning, prompt tuning, instruction tuning, and tool use (e.g., LangChain, LlamaIndex).
Contribute to IP and internal assets for reusable components and accelerators.
Design robust benchmarking and evaluation pipelines for LLM outputs (e.g., factual accuracy, hallucination rates, usefulness).
Ensure responsible AI practices—bias detection, safety constraints, and interpretability.
Collaborate with AI Governance and MLOps teams to ensure scalable and auditable deployments.
Work closely with solution architects, UI engineers, and domain experts to define end-to-end product flows.
Translate business requirements into technical blueprints and iterate through prototypes to production.
Provide technical mentorship to junior data scientists and AI engineers.