This intermediate role focuses on establishing and implementing new application systems in coordination with the Technology team
Job Summary
This intermediate role focuses on establishing and implementing new application systems in coordination with the Technology team.
Candidates must possess deep hands-on experience with Retrieval-Augmented Generation (RAG) pipelines and leading Large Language Models like Gemini and Claude.
The position requires proven ability to deploy GenAI-based models to production environments using robust MLOps principles and Kubernetes.
Matching Summary
This intermediate role focuses on establishing and implementing new application systems in coordination with the Technology team.
Skills & Requirements
Must-have
Generative AI and LLM expertise
Retrieval-Augmented Generation (RAG) pipelines
Python programming with LangChain and LlamaIndex
Vector database integration (Pinecone, Neo4j)
MLOps and CI/CD deployment pipelines
Kubernetes and container orchestration
Nice-to-have
Prompt engineering strategies and tuning
Agentic framework implementation
Guardrails for GenAI safety assessment
Knowledge graph integration
Cross-functional collaboration skills
Key Requirements
4-8 years of relevant Apps Development or systems analysis experience
Bachelor's degree required; Master's preferred
Strong foundational knowledge in Machine Learning and Data Science