Data Scientist

Role Overview

We are seeking a highly motivated and technically sound Data Scientist with 3–4 years of experience in designing, building, and scaling intelligent systems. The ideal candidate will bring hands-on expertise in Generative AI (GenAI), Retrieval-Augmented Generation (RAG), vector databases, and cloud-native applications. A deep understanding of the mechanics, ethical considerations, and trade-offs involved in deploying AI technologies in production environments is essential.

Key Responsibilities

• Design, prototype, and deploy AI/LLM-enabled applications with a focus on modularity, scalability, and maintainability.

• Develop and fine-tune RAG pipelines tailored for enterprise or product-based use cases, optimizing for retrieval relevance and accuracy.

• Work with unstructured data — documents, transcripts, APIs — and transform them into structured knowledge systems using embeddings, vector stores, and knowledge graphs.

• Evaluate and select appropriate GenAI tools, frameworks, and models, balancing performance, latency, cost, and data security.

• Monitor application behavior and refine prompts, retrieval logic, and context engineering to enhance response quality.

• Identify and communicate system edge cases and risks — including hallucination, bias, prompt injection, and model over-reliance.

• Stay current with cutting-edge AI research, frameworks, and methodologies, and assess their potential for production use.

• Collaborate with cross-functional teams including product, design, and backend to integrate intelligent features seamlessly.

• Document technical decisions, model behavior, system limitations, and assumptions in a clear and accessible manner.

Required Skills & Experience

• 3–4 years of hands-on experience in AI/ML, data engineering, or full-stack development, with a strong foundation in intelligent system design.

• Deep understanding of Large Language Models (LLMs) — including concepts like embeddings, context windows, transformers, token limits, and memory strategies.

• Proven experience implementing RAG pipelines and working with vector databases and Knowledge Graphs.

• Familiarity with GenAI frameworks and orchestration tools such as LangChain, LlamaIndex, or similar.

• Proficiency in Python and experience developing backend logic and integrating with APIs.

• Awareness of ethical and security concerns in AI — including safety, fairness, and responsible deployment.

• Ability to assess and articulate trade-offs in system design (e.g., cloud vs. edge deployment, fine-tuning vs. prompt engineering, open vs. closed models).

Preferred Qualifications (Nice to Have)

• Exposure to open-source LLMs and toolkits (e.g., Llama, Mistral, Claude, etc.).

• Experience with evaluation metrics, benchmarking, or implementing human-in-the-loop feedback systems.

• Familiarity with AI observability, logging, and monitoring best practices.

• Contributions to the AI community, such as open-source projects, research papers, blog posts, or talks.

What We Offer

• Competitive compensation and performance-based bonuses

• Opportunities for learning, research, and career growth in cutting-edge AI technologies

• A collaborative, transparent, and inclusive work environment

Skills

1. Generative AI (GenAI)

2. RAG pipelines

3. Python

4. API integration

5. AI/LM