Data Scientist

Position:

Experience:

Location:

Key Responsibilities:

• Design, develop, and optimize LLM-powered systems

  with a

  core focus on Retrieval-Augmented Generation (RAG)

  , prompt engineering, fine-tuning, and agent-based solutions.
• Lead the end-to-end development lifecycle of GenAI applications using

  LangChain / LangGraph

  , ensuring scalable and modular architecture.

• Own and maintain high-quality Python codebases

  , with a strong emphasis on reusable components, performance, and integration with vector databases or search engines used in RAG systems.
• Deploy and optimize solutions on

  cloud platforms (Azure, AWS, or GCP)

  using best practices in CI/CD, containerization, and cost optimization.
• Collaborate cross-functionally with product managers, SMEs, data scientists, and other engineers to gather requirements and deliver business-aligned AI solutions.

• Mentor junior engineers

  and guide best practices in working with LLMs and RAG pipelines.
• Stay up to date with the latest in

  LLM research, RAG frameworks, embeddings, vector stores

  , and transformer-based model capabilities to continuously enhance solution effectiveness.

Required Skills:

• 5+ years

  of professional experience building machine learning systems and data-intensive applications.

• Minimum 1+ year of deep, hands-on experience with LLMs and Generative AI

  , including:

• Mandatory: Expertise in Retrieval-Augmented Generation (RAG)

• Prompt engineering and tuning
• LLM orchestration and agent-based systems

• Strong command of Python

  , with practical experience using

  LangChain

  or

  LangGraph

  in production environments.
• Solid understanding of

  vector databases

  (e.g., FAISS, Pinecone, Weaviate, Chroma) and

  embedding techniques

  .
• Experience in

  SQL

  and integrating structured/unstructured data with GenAI pipelines.
• Proven track record deploying AI applications on

  AWS, Azure, or GCP

  .
• Excellent communication and collaboration skills; ability to work with technical and non-technical stakeholders.
• Prior experience leading technical initiatives or mentoring team members is a strong plus.