AI Engineer
Our customer design and deliver bespoke AI solutions that combine state-of-the-art models with robust, production-grade engineering. We don’t believe AI is magic—but when it’s built thoughtfully and executed well, it can feel that way. We’re seeking a hands-on builder who is excited to push the boundaries of what AI can do, while grounding innovation in strong full-stack engineering principles.
What You’ll Do
- Design, prototype, and scale AI-native applications and agent-based systems that drive real business outcomes.
- Work end-to-end across the stack, including front-end development (React, TypeScript), backend services (Python, Node.js, Go), APIs, and data stores (SQL, NoSQL, and vector databases).
- Build and optimize LLM-driven workflows, leveraging techniques such as retrieval-augmented generation (RAG), embeddings, multi-agent orchestration, and effective context management.
- Architect, deploy, and maintain infrastructure, including CI/CD pipelines, Kubernetes, cloud services, and observability tooling.
- Move efficiently from proof-of-concept to production, balancing speed with scalability, security, and long-term maintainability.
- Continuously optimize AI systems for accuracy, performance, latency, and cost efficiency.
- Partner closely with customers, engineers, product managers, and designers to translate experimentation into reliable, production-ready features.
- Stay hands-on with modern, developer-first tools such as Cursor, Claude Code, GitHub Copilot, and similar platforms to maximize productivity.
About You
- 5+ years of professional software engineering experience, including at least 2 years building AI-powered systems.
- Strong full-stack background, with experience in modern front-end frameworks (React, TypeScript), backend development (Python, Node.js, Go), and a range of databases (SQL, NoSQL, vector stores).
- Familiarity with AI and LLM development tools such as Cursor, Claude Code, GitHub Copilot, LangChain, CrewAI, or comparable frameworks.
- Hands-on experience with cloud-native architectures (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD workflows, monitoring, and scalable systems.
- Solid understanding of the LLM lifecycle, including prompting strategies, evaluation, fine-tuning, embeddings, RAG, and agent design.
- A pragmatic engineering mindset—you recognize that reliable AI systems require testing, observability, safeguards, and fallback logic, not just clever prompts.
- Strong communication and collaboration skills, with the ability to bridge technical depth and business context.
- Curiosity and enthusiasm for exploring new ideas, paired with a commitment to delivering production-quality software.
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