
Full-Stack AI Engineer
2 weeks ago
We are looking for a highly skilled Full-Stack AI Engineer who can design, develop, and deploy advanced AI-driven applications. The ideal candidate will have strong expertise in RAG (Retrieval-Augmented Generation) systems, vector databases , and end-to-end ML pipelines . You will be working on scalable, production-grade AI solutions leveraging state-of-the-art frameworks and cloud-native technologies.
Key Responsibilities
- Design and implement RAG-based AI applications with optimized semantic search.
- Build and manage vector database solutions (Pinecone, Weaviate, Qdrant, FAISS).
- Develop and maintain production-ready ML systems ensuring performance and scalability.
- Apply prompt engineering techniques to fine-tune model interactions.
- Handle document chunking, embedding generation, and retrieval optimization .
- Integrate LangChain/LlamaIndex frameworks for orchestration.
- Implement caching strategies (Redis or similar) for efficiency.
- Collaborate with cross-functional teams to deliver end-to-end AI solutions.
Core Skills
- Strong experience in RAG implementation .
- Expertise in vector databases (Pinecone, Weaviate, Qdrant, FAISS).
- Proficiency in semantic search optimization .
- Hands-on knowledge of production ML system deployment .
Technical Requirements
- Proficient in LangChain, LlamaIndex .
- Experience with OpenAI APIs, Hugging Face, Sentence Transformers .
- Strong programming skills in Python, NumPy , and ML libraries.
- Familiarity with prompt engineering best practices.
- Knowledge of document chunking techniques for retrieval systems.
- Experience with Redis or other caching mechanisms .
Preferred Qualifications
- 3–6 years of experience in AI/ML engineering or full-stack development .
- Strong problem-solving and optimization skills.
- Experience working in fast-paced startup or product environments .
- Knowledge of cloud deployment (AWS, GCP, or Azure) is a plus.
Why Join Us?
- Work with cutting-edge Generative AI and Retrieval systems .
- Opportunity to contribute to high-impact AI products in production.
- Collaborative, growth-oriented, and innovative work culture.
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