Job Description

Role: Data Scientist

Location: Atlanta, GA- Hybrid

Experience 5 to 8 years max (Not more than 8 years)

Annual Salary + benefits

No OPT, CPT Consultants

Job description:

We re seeking a Junior Data Scientist with hands-on experience in agentic AI systems, large language models (LLMs), and transformer-based architectures. As a member of our Digital Solutions team, you ll contribute to building and optimizing intelligent systems that reason, adapt, and act autonomously. This is a dynamic role suited for candidates who are eager to innovate with state-of-the-art language models and vector-based search technologies.

Key Responsibilities

• Design and optimize prompts for task-specific performance across Claude, GPT, LLaMA, and open-source LLMs.
• Work on retrieval-augmented generation (RAG) pipelines leveraging vector search (e.g., Pinecone, Weaviate, FAISS, Chroma, pgvector etc).
• Convert unstructured data (e.g., PDFs, scanned docs, images) into structured formats using OCR, document parsing, document classification, and document centric Vision LLMs.
• Build and maintain agentic AI workflows using frameworks like LangChain, LangGraph, or AutoGen.
• Develop autonomous agent systems capable of multi-step reasoning and execution.
• Fine-tune foundation models (e.g., LLaMA, BERT, GPT, Mistral) using Hugging Face Transformers, OpenAI APIs, or LangChain.
• Apply transformer architectures and embedding techniques to domain-specific problems.
• Collaborate with cross-functional teams including senior ML engineers and product managers to deliver scalable GenAI solutions.
• Stay up-to-date with advancements in LLM fine-tuning, RAG strategies, and autonomous agent research.

Required Qualifications

• Bachelor s or Master s degree in Computer Science, Data Science, Machine Learning, or a related technical field.
• 2 5 years of experience in AI/ML Applications, GenAI development, or LLM-focused roles.
• Hands-on experience with prompt engineering and LLM-based task chaining.
• Experience extracting structured data from unstructured documents using popular Python libraries such as pytesseract, EasyOCR, Doctr, vision-parse, LayoutLM, Donut.
• Proficiency in Python, including ML libraries like PyTorch, Hugging Face Transformers, Pandas, Scikit-learn.
• Working knowledge of agentic AI frameworks (LangChain, AutoGen, LangGraph or CrewAI).
• Experience querying and integrating vector databases (e.g., Pinecone, Weaviate) for semantic search and RAG.
• Strong foundation in data analysis and ability to extract useful insights to guide from LLMs.

Nice to Have

• Experience working with Palantir Foundry or AIP platforms.
• Understanding of prompt engineering and instruction optimization.
• Experience integrating LLMs into production pipelines.
• Exposure to reinforcement learning or self-improving AI agents.
• Contributions to open-source LLM/AI projects.

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