AI Engineer

# Who we are

White Rabbit Group is a company that partners with world-class agencies for web and mobile app development and design, letting our customers focus on what they do best, while we focus on building. We work across a broad spectrum of technologies including PHP, React, Node, Strapi, Shopify, WordPress, and fully native and hybrid mobile apps. With nearly 100 full-time employees across the US, Colombia, and India, we collaborate closely to bring our customers’ visions to life—and have fun doing it!

# What we’re looking for

Join us to help design, build, and scale the next generation of AI-powered software solutions.

We’re seek an AI Engineer who combines strong software engineering fundamentals with hands-on expertise in Generative AI, Large Language Models (LLMs), agentic workflows, and AI-assisted software development. This is a highly technical, hands-on role where you’ll architect production-ready AI systems, establish engineering standards, and build reusable AI frameworks that accelerate software delivery across the organisation. You’ll lead the implementation of AI-powered solutions while mentoring engineers, improving engineering practices, and driving AI adoption throughout the software development lifecycle by working closely with AI Application Engineers and other engineering departments in the organisation.

If you’re passionate about solving complex engineering problems, building reliable AI systems, and continuously exploring emerging AI technologies, we’d love to hear from you!

# Responsibilities

– Design, develop, and maintain production-grade AI applications, including AI agents, Retrieval-Augmented Generation (RAG) solutions, AI assistants, and workflow automation systems.
– Architect scalable AI solutions that balance performance, reliability, maintainability, security, cost efficiency, and user experience.
– Define and maintain AI engineering standards, including prompt engineering practices, reusable templates, AI workflows, guardrails, and validation frameworks.
– Build reusable AI accelerators, internal frameworks, and engineering tools that improve productivity across software delivery teams.
– Design and optimize agentic workflows, prompt strategies, and AI orchestration patterns to improve solution quality and engineering efficiency.
– Establish evaluation frameworks to validate AI outputs through regression testing, benchmarking, automated quality checks, and continuous improvement.
– Evaluate AI-generated outputs for accuracy, reliability, security, and compliance with business requirements before production deployment.
– Collaborate with architects, delivery teams, and engineering leadership to translate business requirements into scalable AI solutions.
– Mentor AI Application Engineers, providing technical guidance, code reviews, prompt reviews, and best practices for AI-assisted development.
– Continuously evaluate emerging AI models, tools, frameworks, and engineering techniques, recommending practical adoption where they deliver measurable business value.
– Enforce software engineering best practices across the development lifecycle, ensuring AI applications are designed with secure authentication, robust APIs, efficient database management, and reliable CI/CD pipelines.
– Effectively manage multiple technical initiatives while maintaining a strong focus on quality, innovation, and delivery excellence.

# Qualifications

– At least 2 years experience in software engineering, focused on AI, LLM-powered applications, or AI-assisted software development.
– Proven experience designing and delivering production-ready AI applications using modern Large Language Models.
– Hands-on experience with AI platforms such as OpenAI, Anthropic, Google Gemini, or equivalent providers.
– Experience using AI engineering tools such as Cursor, Claude Code, GitHub Copilot, or similar AI-assisted development environments.
– Proficiency in Python and experience integrating AI capabilities through APIs and SDKs.
– Experience deploying AI-powered applications on AWS, Azure, or Google Cloud Platform.

# Bonus Points

– Experience with specific AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, or similar platforms.
– Familiarity with cutting-edge protocols like Model Context Protocol (MCP), multi-agent architectures, and advanced AI workflow design.
– Hands-on experience using dedicated AI observability and tracing tools such as LangSmith, Langfuse, or similar platforms.
– Exposure to multi-model AI architectures, intelligent model routing, caching strategies, and AI cost/