Frontier Engineer

As a Frontier Engineer, you'll help build AI that moves the world forward. Working at the frontier of technology, you'll join a lean, high-impact pod where human ingenuity and AI come together to solve real-world challenges for some of the world's leading organizations. You'll partner directly with clients to design, deploy and continuously improve AI-native systems, with the freedom to experiment with emerging technologies and turn bold ideas into real impact.

• Build and orchestrate multi-agent AI systems, integrating agent outputs into production environments that solve real business problems.

• Design intent-driven prompts and workflows for AI coding agents, applying human judgment to validate quality, safety and accuracy.

• Create scalable RAG architectures, vector stores and knowledge systems that enable trustworthy and context-aware AI experiences.

• Deploy, monitor and optimize LLMs and AI agents in production, implementing responsible AI guardrails and observability practices.

• Build AI-embedded applications and workflows using enterprise AI platforms, automation tools and modern engineering practices.

We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days a week in a client or Cognizant office in London, United Kingdom. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

• 3-10 years of technical delivery experience with strong AI-tool fluency across the full stack.

• Hands-on experience with agentic coding and multi-agent orchestration using frameworks such as Lang Chain, Auto Gen or Lang Graph.

• Strong prompt and intent engineering skills, with the ability to validate AI-generated code for quality and correctness.

• Experience designing RAG pipelines and managing vector databases to ground and improve agent accuracy.

• LLMOps and model lifecycle management experience, including guardrail implementation, AIOps observability and CI/CD for AI systems.

• Demonstrated agentic engineering and orchestration expertise paired with strong context and retrieval engineering.

• Production AI operations (LLMOps) experience running LLMs and agents at scale.

• A track record of full-stack AI delivery, reaching for an AI tool first as a generalist builder.

• Domain fluency and contextual judgment to validate AI outputs against real business context.

• A strong business-outcome orientation, owning what the pod builds permanently in production.