Responsibilities:
Technical Leadership & System Architecture
- Lead the architecture and development of LLM-driven applications, AI agents, and RAG-based systems.
- Provide technical guidance, conduct code reviews, and mentor junior team members.
- Drive best practices in Python backend engineering, API development, and AI system design.
Backend Engineering (Python)
- Build and maintain backend services using FastAPI or Flask.
- Develop scalable API endpoints for AI applications, embeddings, and retrieval systems.
- Ensure backend code quality, modularity, performance, and maintainability.
LLMs, RAG, and AI Agent Development
- Build AI applications using: LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen
• Develop autonomous or semi-autonomous AI agents with tool calling and workflow graphs.
• Implement Retrieval-Augmented Generation (RAG), embedding pipelines, chunking strategies, reranking, and grounding techniques.
• Work with OpenAI SDK and other LLM providers (Anthropic, Azure OpenAI, Cohere, etc.).
• Manage prompt engineering, prompt routing, safety guardrails, and evaluation metrics.
Data & Vector Search Engineering
• Build data pipelines for indexing, embeddings, and retrieval workflows.
• Work with SQL databases (PostgreSQL, MySQL, etc.) for metadata and application storage.
• Work with vector databases such as: Redis, Postgres with pgvector, Elasticsearch, Neo4j, or others.
• Implement and optimize search workflows using FAISS or similar similarity search libraries.
MLOps, Deployment & Observability
• Deploy AI services using Docker, container orchestration, and cloud environments.
• Implement monitoring for AI behavior, performance, error rates, and retrieval accuracy.
• Set up CI/CD pipelines for backend and AI components.
• Optimize inference cost, latency, and reliability.
Requirements:
• Bachelor’s/Master’s degree in Computer Science, AI/ML, Data Science, or related fields.
• 10+ years of experience in Python backend development.
• Strong proficiency in FastAPI or Flask.
• Strong working knowledge of SQL databases (Postgres, MySQL, etc.).
• Hands-on expertise with vector databases:
Redis, Postgres/pgvector, Elasticsearch, or Neo4j.
• Practical experience with FAISS for similarity search.
• Hands-on experience with modern LLM frameworks:
LangChain, LangGraph, Semantic Kernel, Haystack, LlamaIndex, AutoGen.
Strong understanding of:
- Embeddings & vector search
- RAG pipelines
- Retrieval optimization
- Chunking strategies
- Document loaders & indexing
• Experience building AI apps using OpenAI SDK or similar.
• Experience deploying APIs/services using Docker and cloud environments.
• Leadership experience: guiding teams, conducting reviews, driving architecture decisions.
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