1 month ago
Staines-upon-Thames, United KingdomSenior
Responsibilities
- Design and build MCP servers over enterprise business objects, including capability modeling, discoverability, versioning, and backward compatibility.
- Build safe agent write paths for changing customer operational data.
- Develop ontology, knowledge graph, semantic, skills, routing, and context layers for enterprise AI agents.
- Build a control plane covering authentication, entitlements, agent identity, telemetry, metering, injection resistance, and deny-by-default controls.
- Create evaluation harnesses that certify agent behavior against the real product and improve systems based on measured results.
- Prototype emerging technologies, product opportunities, and customer scenarios.
- Establish engineering practices for evaluation, testing, observability, monitoring, governance, security, and operational excellence.
- Contribute to technical design, review engineering work, support colleagues, and represent the work in customer engagements, demonstrations, industry events, and partner collaborations.
Requirements
- Strong software engineering background with production experience building and operating enterprise systems.
- Deep experience with distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability, and CI/CD.
- Strong programming ability in a modern backend language.
- Hands-on experience designing, building, and shipping production AI applications using LLMs, RAG, agentic workflows, orchestration frameworks, tool use, function calling, and autonomous or multi-agent architectures.
- Experience with AI evaluation practices including experimentation, benchmarking, prompt engineering, tracing, quality measurement, and agent tuning.
- Ability to integrate enterprise applications, business processes, workflows, and data platforms.
- Depth in tool-surface and agent-runtime engineering, knowledge graphs and semantic modeling, or enterprise platform engineering.
- Experience with MCP servers, tool ecosystems, capability modeling, ontology design, context engineering, embeddings, vector databases, retrieval, search, memory architectures, grounding, or metadata-driven enterprise systems.
- Experience with Oracle PL/SQL and OData is desirable for enterprise platform work.
- Experience with agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, OpenAI Agents SDK, or CrewAI is desirable.
- Experience building reusable AI platforms, MCP ecosystems, or shared engineering capabilities across products and teams is desirable.
- Experience with Docker, Kubernetes, Azure, AWS, GCP, or another hyperscale cloud platform is desirable.
- Experience with reverse engineering, interpreter work, token-efficient agent design, relevant enterprise software domains, open source, technical communities, conferences, publications, or standards is desirable.
Benefits
- Flexible hybrid work opportunities and workplace arrangements designed to support diverse needs.
- Opportunity to work on production AI systems combining LLMs, agentic AI, and cloud-native technologies for enterprise-scale applications.
- Inclusive, global work environment with opportunities for customer, partner, industry, and technical community engagement.
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