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Intelligent System Architect

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Job Title:

Data Developer

Overview

We are seeking a highly motivated and technically skilled Data Developer 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.

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