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. In this hands-on role, you will work under the guidance of senior developers and AI architects to help build retrieval-grounded, context-aware, and increasingly agentic AI applications. You will contribute to reliable, AI-driven features while growing your expertise across the modern GenAI stack. This role focuses on applying pre-trained and hosted foundation models — through context engineering, RAG, knowledge graphs, and agentic workflows — rather than training or fine-tuning models.

This is a growth-oriented role: you’ll take ownership of well-scoped components, learn established patterns from senior engineers, and progressively increase your technical depth and independence.

Key Responsibilities

• Assist in building and integrating generative AI applications using pre-trained and hosted foundation models (via managed GenAI APIs and open-model endpoints).
• Support the implementation of context engineering workflows — assembling system instructions, retrieved knowledge, tool definitions, and conversation memory into reliable, token-efficient prompts, following established patterns.
• Contribute to prompt engineering (zero-shot, few-shot, chain-of-thought, role-based prompting) for AI-powered workflows.
• Help develop and maintain Retrieval-Augmented Generation (RAG) components, including chunking, embedding, and semantic/keyword search.
• Support the development of knowledge graph and Graph RAG pipelines under guidance to enable grounded, traceable responses.
• Contribute to agentic workflows — helping build AI agents with tool-calling and basic planning/memory, using frameworks such as Google Agent Development Kit (ADK), LangGraph, or CrewAI.
• Assist with integrating agents to external tools and data sources via the Model Context Protocol (MCP), with exposure to the Agent2Agent (A2A) protocol.
• Support the deployment, monitoring, and maintenance of GenAI and agentic applications in production environments.
• Perform data preprocessing, document ingestion, and basic API development for AI applications.
• Collaborate with data scientists and engineers to integrate AI capabilities into products.
• Participate in code reviews, testing, and documentation to ensure quality and reliability.
• Stay curious about advancements in GenAI and agentic AI, and share learnings with the team.

Required Technical Skills

• 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.
• Awareness of agent tooling and protocols, including tool/function calling and the Model Context Protocol (MCP); familiarity with the A2A protocol is a plus.
• 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.

Required Soft Skills

• 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.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 4+ years of experience with min 2–4 years of professional experience in software/AI development, with exposure to Generative AI and agenti…