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
Work Location: In person
