Agentic AI/LLM, RAG Developer

About the Role

The Agentic AI/LLM, RAG Developer will design, build, and optimize applications that use large language models, agentic workflows, and Retrieval-Augmented Generation (RAG) to solve real-world problems.

Key Responsibilities

• Architecting LLM-based systems.
• Implementing RAG pipelines (document ingestion, indexing, retrieval, and generation).
• Integrating these solutions with existing products and APIs.
• Collaborating with product and data teams to define requirements.
• Conducting experiments to evaluate models and prompting strategies.
• Continuously improving performance, reliability, and safety of AI features.
• Testing, monitoring, and maintaining production-grade AI services, including handling data quality, latency, and observability.

Requirements & Tech Stack

• Strong programming skills in languages commonly used for AI/ML and backend services (such as Python, TypeScript/JavaScript, or similar).
• Experience with modern software development practices (testing, version control, CI/CD).
• Hands-on experience working with large language models and agentic frameworks, including prompt engineering, tool/function calling, workflow orchestration, and evaluation of LLM outputs.
• Knowledge of Retrieval-Augmented Generation (RAG) architectures, including document preprocessing, embeddings, vector databases, and retrieval strategies for building efficient question-answering or assistant systems.
• Experience with cloud platforms and infrastructure (e.g., AWS, GCP, Azure).
• Familiarity with deploying, monitoring, and scaling AI-powered services in production environments.
• Understanding of fundamental machine learning and NLP concepts, including model evaluation, data quality, and techniques for improving relevance, robustness, and safety of AI systems.
• Ability to collaborate with cross-functional teams.
• Ability to translate product requirements into technical designs.
• Ability to communicate complex technical ideas clearly and concisely.
• Experience working in a startup.

Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.