Intelligent System Developer

4 days ago

Shimoga, Karnataka, India beBeeData Full time ₹ 10,00,000 - ₹ 15,00,000

We are seeking a highly skilled and technically proficient individual to join our team as a Data Scientist.

The ideal candidate will have hands-on expertise in designing, building, and scaling intelligent systems, with a strong foundation in Generative AI, Retrieval-Augmented Generation, 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 for success in this role.

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.

Requirements:

  1. 3–4 years of hands-on experience in AI/ML, data engineering, or full-stack development, with a strong foundation in intelligent system design.
  2. Deep understanding of Large Language Models (LLMs) — including concepts like embeddings, context windows, transformers, token limits, and memory strategies.
  3. Proven experience implementing RAG pipelines and working with vector databases and Knowledge Graphs.
  4. Familiarity with GenAI frameworks and orchestration tools such as LangChain, LlamaIndex, or similar.
  5. Proficiency in Python and experience developing backend logic and integrating with APIs.
  6. Awareness of ethical and security concerns in AI — including safety, fairness, and responsible deployment.
  7. 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).

Nice-to-Have Qualifications:

  • 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.

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