Requirements Summary

Strong teamwork and communication abilities, eagerness to learn new AI/GenAI and agentic technologies and frameworks, analytical mindset and attention to detail, openness to feedback, coaching, and continuous improvement

Benefits

Education

Master's degree

Full Job Description

Role Overview We are looking for an enthusiastic Generative AI Developer to join our Controls Technology team and support the development and integration of generative AI solutions. This is a growth-oriented role where you'll take ownership of well-scoped components, learn established patterns from senior engineers, and progressively increase your technical depth and independence. What You Will Do Assist in building and integrating generative AI applications using pre-trained and hosted foundation models, support the implementation of context engineering workflows, contribute to prompt engineering, and help develop and maintain Retrieval-Augmented Generation (RAG) components. Why It Might Be a Fit You will have the opportunity to work with a team of senior developers and AI architects, contribute to reliable AI-driven features, and grow your expertise across the modern GenAI stack. Requirements Proficiency in Python for GenAI development, data preprocessing, and scripting. Solid understanding of core generative AI concepts — foundation models, LLMs, tokenization, embeddings, and context windows. Hands-on experience with prompt engineering; foundational understanding of context engineering techniques. Practical experience (project or professional) building RAG systems, including chunking, vector databases, and semantic search. Familiarity with knowledge graphs and interest in Graph RAG for relationship-aware retrieval. Exposure to agentic AI development — building tool-using agents or multi-step workflows with a framework such as Google ADK, LangGraph, CrewAI, or the OpenAI Agents SDK. Basic understanding of agent harness concepts — session/state management, memory, and guardrails. Experience consuming major GenAI APIs (e.g., OpenAI, Gemini, Claude) and exposure to orchestration frameworks such as LangChain or LlamaIndex. Understanding of application deployment and containerization (Docker). Working knowledge of version control systems (Git). Awareness of AI compliance, data privacy, guardrails, and responsible AI principles. Benefits Competitive salary Equity Health insurance Paid time off Retirement plan Learning budget Parental leave Wellness program Visa/relocation assistance Remote flexibility Stipends Bonus/commission Paid holidays

Skills and Technologies