
Data Architect
1 week ago
We are seeking a highly motivated and technically proficient Data Architect to design, build, and scale intelligent systems. The ideal candidate will bring hands-on expertise in Generative AI (GenAI), RAG pipelines, vector databases, and cloud-native applications.
- 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.
Key Responsibilities:
- 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).
- 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.
- 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
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