You will be responsible for taking concepts from initial PoCs to production-ready, scalable RAG (Retrieval-Augmented Generation) applications.

Key Responsibilities :

- End-to-End AI Engineering : Architect and build full-stack applications that leverage Large Language Models (LLMs) to solve complex business problems.

- RAG Architecture : Design and optimize Retrieval-Augmented Generation pipelines, ensuring high-quality context retrieval through advanced embedding techniques and vector databases.

- Rapid Prototyping : Drive the innovation cycle by building high-fidelity Proof of Concepts (PoCs) for new AI-powered features.

- API & Backend Systems : Develop high-performance APIs and microservices (using Python/FastAPI or Node.js) to manage AI inference workflows and data processing.

- LLM Orchestration : Integrate and fine-tune interactions with various LLM providers (OpenAI, Anthropic, or Open Source via HuggingFace) using orchestration frameworks.

Technical Requirements :

AI & LLM Specialization :

Frameworks : Proficiency in LangChain, LlamaIndex, or Haystack.

- Vector Databases : Hands-on experience with Pinecone, Milvus, Weaviate, or ChromaDB.

- Models : Deep understanding of prompting, context window management, and model selection across OpenAI, Claude, and Llama 3.

- Concepts : Solid grasp of semantic search, tokenization, and embedding models.

Full-Stack Engineering :

Backend :

3+ years of experience in Python (FastAPI/Flask) or Node.js. Knowledge of .NET is a strong plus.

- Frontend : Expertise in React.js or Next.js for building responsive, AI-driven user interfaces.

- Cloud & DevOps : Familiarity with Docker and deployment on cloud platforms (AWS, Azure, or GCP).

- Databases : Experience with both SQL (PostgreSQL) and NoSQL environments.

Preferred Qualifications :

- Proven track record of building and deploying AI side projects or commercial PoCs.

- Experience with Agentic Workflows (e.g., CrewAI or AutoGen).

- Strong understanding of AI evaluation metrics and monitoring.