AI Solution Engineer

We are looking for engineers who can move fast across the full AI stack, from applied research/benchmarking and prototyping to production deployment, and who bring both technical depth and the curiosity to keep pace with a rapidly evolving landscape.

Responsibilities

• Design and build LLM-powered applications using frontier models (OpenAI, Anthropic, Gemini) and open-source alternatives, across the full stack from data ingestion and vector stores through to model serving and deployment
• Architect agentic workflows including tool calling, memory, planning, and multi-agent orchestration using frameworks such as LangGraph and CrewAI, as well as with native LLM primitives
• Integrate AI systems with external tools, services, and data sources using primitives like A2A, MCP, and API function calling
• Rapidly prototype and iterate using agentic coding tools such as Claude Code, Codex, and Antigravity to accelerate development cycles
• Apply prompt engineering, RAG pipelines, and LLM evaluation techniques to build reliable, production-grade AI systems
• Translate complex business problems into practical AI solutions, working both as part of cross-functional teams and as an independent contributor with end-to-end ownership
• Scan the landscape to identify state-of-the-art frontier as well as open weight models, and make recommendations for specific solutions

Requirements / What We're Looking For

• Strong hands-on experience building LLM-powered applications, with working knowledge of at least one frontier model provider and familiarity with open-source alternatives
• Demonstrated ability to architect AI-native solutions and pipelines end-to-end
• Experience designing agentic systems, with practical knowledge of agent frameworks, orchestration patterns, and integration primitives
• Ability to synthesize data from disparate systems and build pipelines that construct rich, reliable context for AI applications
• Familiarity with at least one major cloud platform (AWS, Azure, or GCP) for AI application deployment
• A self-driven learning orientation, actively following developments in LLMs, agent frameworks, and the broader AI ecosystem
• 10+ years of experience architecting and building robust, enterprise-grade systems, with a track record of taking complex solutions from concept to production at scale