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Principal Intelligent Systems Developer

2 weeks ago

Aurangabad, Maharashtra, India beBeeDataScientist Full time ₹ 1,00,00,000 - ₹ 1,50,00,000
Intelligent System Design

Role Overview

Seek a highly motivated and technically skilled individual to design, build, and scale intelligent systems.

Key Responsibilities:

  • Design, prototype, and deploy AI/LLM-enabled applications for modularity, scalability, and maintainability.
  • Develop and fine-tune RAG pipelines tailored for enterprise or product-based use cases, optimizing retrieval relevance and accuracy.
  • Transform unstructured data into structured knowledge systems using embeddings, vector stores, and knowledge graphs.
  • Evaluate 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 system edge cases and risks, including hallucination, bias, prompt injection, and model over-reliance.
  • Stay current with cutting-edge AI research and assess its potential for production use.
  • Collaborate with cross-functional teams to integrate intelligent features seamlessly.
  • Document technical decisions, model behavior, system limitations, and assumptions clearly.

Required Skills & Experience:

  • 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 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 like 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 trade-offs in system design.

Preferred Qualifications:

  • Exposure to open-source LLMs and toolkits.
  • Experience with evaluation metrics, benchmarking, or human-in-the-loop feedback systems.
  • Familiarity with AI observability, logging, and monitoring best practices.
  • Contributions to the AI community through open-source projects, research papers, blog posts, or talks.