Key Responsibilities
Your key responsibilities would inlcude:
Build and deploy production-ready GenAI and Agentic AI systems using LLMs, RAG, and AI workflows.
Develop scalable Python backends, APIs, and data pipelines for AI applications.
Design RAG and hybrid search systems across vector databases, knowledge graphs, and unstructured data.
Build agentic workflows using frameworks such as LangGraph, AutoGen, or CrewAI.
Evaluate and optimize AI systems through prompt engineering, RAG evaluation, and model optimization.
Translate business requirements into scalable, reliable, and production-ready AI solutions.
Skills Required
Must-Have:
Strong Python + backend engineering
Hands-on experience with LLMs and RAG systems
Experience with APIs, data pipelines, and unstructured data
Good-to-Have (Differentiators):
Knowledge Graphs (Neo4j, Cypher, RDF/SPARQL)
Agent frameworks (LangGraph, AutoGen, CrewAI)
Hybrid search (Graph + Vector)
LLM evaluation / optimization (RAGAS, prompt tuning, fine-tuning)
Qualifications
Preferred Qualifications:
Bachelor’s degree in Computer Science, Engineering, or a related field
3–8 years of experience in backend engineering, AI, or data-driven systems
Strong Python and backend development experience
Hands-on experience with LLMs, RAG pipelines, and AI system design
Experience working with APIs, data pipelines, and unstructured data
What Makes This Role Exciting:
Build real-world GenAI systems, not just demos
Work across LLMs → RAG → Graphs → Agents
Opportunity to shape architecture and offerings from the ground up