Requirements

Bachelor’s degree in Computer Science, Mathematics, or a related quantitative field, or equivalent practical experience
10+ years of post-graduation engineering experience with proficiency in Python, TypeScript/JavaScript, Java, or C++
5+ years of experience applying AI/ML in production, including generative/agentic AI applications, RAG systems, or evaluation pipelines
Strong problem-solving skills with a track record of developing prototypes from ambiguous early-stage problems to production
Deep expertise in system design and architecture, with experience in cloud-based machine learning tools and platforms (AWS, GCP, Azure)
Experience mentoring engineers and elevating technical standards within a team or organization
Comfort working directly with clients at all levels, including engineering teams and senior executives
Excellent written and verbal communication skills for cross-functional collaboration

Nice to Haves

Experience in a forward deployed engineering role
Startup experience, especially as a founding engineer
Experience building and deploying large-scale AI solutions
Experience delivering technology programs for governments or regulated industries
Experience deploying solutions on-premises or in customer-controlled environments
Current or prior UK government security clearance (e.g. SC or DV)

Tackling the hardest technical problems across programs and stepping in to resolve stuck projects or unclear paths
Leading technical aspects of early engagements by scoping problems with clients and shaping proofs of concept
Building working prototypes rapidly, ranging from AI agents to evaluation pipelines and knowledge systems, and advancing promising prototypes to production
Defining the underlying technology and architecture to ensure solutions are cohesive, secure, and reusable across clients
Mentoring engineers through pairing, design and code reviews, and raising engineering excellence throughout the organization
Collaborating with Directors, VPs, and client executives to navigate business and technical trade-offs, including travel to client sites as needed
Transforming field insights into reusable capabilities and contributing components back to the core platform
Leading advancements in agentic engineering both in AI systems and everyday team practices, pushing AI boundaries