
AI Innovation Lead
2 days ago
As a seasoned professional in intelligent system design, you have the unique opportunity to drive innovation and growth within our cutting-edge organization.
We are seeking an accomplished 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.
- 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).
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
Key Technologies:
- Generative AI
- RAG Pipelines
- Python
- API Integration
- AI/LM
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