GenAI Developer

Role & responsibilities

AI & Data Integration: Design and implement AI architectures that connect structured and unstructured data sources to LLMs using APIs and vector databases. • API Development: Build and maintain secure, scalable API endpoints that facilitate seamless data exchange between databases, AI models, and front-end reporting systems. • LLM Pipeline Engineering: Develop and optimize retrieval-augmented generation (RAG) pipelines, context encapsulation layers, and prompt engineering frameworks to deliver consistent and accurate outputs from AI models. • Data Management: Lead ETL/ELT data processing workflows to ensure data quality, consistency, and readiness for AI-driven analytics. • Reporting Automation: Build dynamic, AI-powered reporting systems and dashboards that translate LLM insights into business intelligence and measurable outcomes. • Cross-functional Collaboration: Work closely with stakeholders across data, analytics, and business units to identify opportunities for AI application and operational improvement.

Preferred candidate profile

Familiarity with LangChain, LlamaIndex, or similar orchestration tools for AI pipelines. • Strong grounding in prompt engineering, context management, and fine-tuning of generative models. Data Engineering: • Proficiency in SQL/NoSQL databases (PostgreSQL, MongoDB, BigQuery).