AI Engineer - Applied

About the role

Key duties

• Convert routine, high-volume business work into AI-assisted workflows supervised by employees.
• Build AI agents with tool definitions, orchestration, state, memory, retries, escalation paths and stopping conditions.
• Integrate LLM and multimodal APIs into applications and extract structured information from PDFs, scans, photos and emails.
• Create evaluations from production data, set confidence thresholds and support evidence-backed staged releases.
• Design human review experiences and capture feedback; monitor tracing, usage, cost, latency and quality.
• Secure AI solutions through prompt-injection mitigation, permission boundaries, audit trails and approval gates.
• Maintain shared AI infrastructure such as MCP servers, tool libraries, versioning, gateways and evaluation tools.
• Document, debug and support maintainable production solutions.

Key selection criteria

• Proven experience shipping LLM-powered systems to real users and supporting them in production.
• Experience designing AI agents, orchestration, state management and human-in-the-loop workflows.
• Strong Python and TypeScript skills; experience building production-ready AI applications and services.
• Experience with prompt/context engineering, structured outputs, evaluation frameworks and confidence-based systems.
• Understanding of MCP, AI security, prompt-injection mitigation and responsible AI.
• Experience managing model choice, token usage, latency, cost and production performance.
• Knowledge of Google Gemini, Anthropic Claude and OpenAI GPT; hands-on Google Cloud experience, especially Vertex AI, Gemini, Cloud Run and BigQuery.
• At least five years building and shipping production software, including APIs, event-driven architecture, CI/CD, infrastructure as code, Git and testing.
• Ability to work across existing technology stacks and contribute to established codebases.
• Vision and multimodal models, document extraction, AI observability, retrieval, vector search or workflow-engine experience is highly regarded.

Technology environment

• Google Cloud is the primary platform; Microsoft Azure is also used.
• AI environment includes Agent Platform, Gemini, Python agents deployed as containerised services, Microsoft 365 and Graph integrations, and third-party model providers.
• Applications include service-based .NET platforms, workflow engines, Angular front ends and established business systems with document and integration workloads.
• Data platforms include SQL Server, PostgreSQL, BigQuery and enterprise BI tools.
• Engineering tools include Azure DevOps, Terraform and Datadog.

Workplace

• Hybrid role based in Richmond, with flexibility to work remotely.

What the employer offers

• The employer highlights stability, career progression, training, mentorship, a collaborative team, and discounts on technology, cars, fashion and leisure.

Recruitment process

• A written technical assessment, criminal background check and medical form part of recruitment.