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AI Platform Engineer, Agentic Interfaces at IFS · Katori

Asks for

The ad says“Evaluation as a discipline: experimentation, benchmarking, prompt engineering, tracing, quality measurement and agent tuning, improving an agent against evidence rather than impression.”
The ad says“Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Strong programming in a modern backend language. See the note on language under “Open calls” before advertising.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.”
The ad says“Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail.”
The ad says“Tool-surface and agent-runtime engineering. MCP servers, tool ecosystems, capability modelling, discoverability, governance, versioning, backward compatibility, multi-tenancy isolation.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Enterprise platform depth. Oracle PL/SQL, OData, and comfort working inside large metadata-driven systems where behaviour is configured rather than coded.”
The ad says“Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.”
The ad says“Enterprise platform depth. Oracle PL/SQL, OData, and comfort working inside large metadata-driven systems where behaviour is configured rather than coded.”
The ad says“Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail.”
The ad says“Evaluation as a discipline: experimentation, benchmarking, prompt engineering, tracing, quality measurement and agent tuning, improving an agent against evidence rather than impression.”
The ad says“Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.”
The ad says“Strong software engineering first. Everything else is applied on top of that.”
The ad says“Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.”

Company Description

At IFS, we're building the next generation of AI-native enterprise software, transforming how some of the world's largest organisations manage assets, operations and critical services.

This is an opportunity to work at the forefront of modern AI engineering, building intelligent products that combine Large Language Models (LLMs), agentic AI and cloud-native technologies to solve complex, real-world business challenges at enterprise scale.

We're looking for engineers who are passionate about building production AI systems and excited by the opportunity to shape the future of enterprise software.

Please note that this role requires demonstrable, hands-on experience designing, building and shipping production AI applications.

IFS is a billion-dollar revenue company with 7000+ employees on all continents. We deliver award-winning enterprise software solutions through the use of embedded digital innovation and a single cloud-based platform to help businesses be their best when it really matters–at the Moment of Service™.

At IFS, we're flexible, we're innovative, and we're focused not only on how we can engage with our customers, but on how we can make a real change and have a worldwide impact. We help solve some of society's greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and accept that there are so many different perspectives in this world. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We're looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs.

If you want to change the status quo, we'll help you make your moment. Join Team Purple. Join IFS.

Job Description

Build and operate the interface, semantic layer and controls that let AI agents work inside IFS software safely.

Key responsibilities

Design and build MCP servers (Model Context Protocol, the emerging standard for exposing application capability to agents) over the product’s business objects, treating capability modelling, discoverability, versioning and backward compatibility as first-class design problems.

Build the write path that lets an agent safely change a customer’s operational data.

Build the semantic layer: an ontology and knowledge graph over the product, generated from what the platform already knows about itself and then curated industry by industry.

Build the skills layer that maps what someone asks for onto the correct operation and the correct sequence, with a router that picks between them.

Build the control plane: authentication, entitlements, agent identity, telemetry, metering, resistance to injection, and a default that denies rather than permits.

Build the evaluation harness that certifies agent behaviour against the real product, and improve the system against what it measures.

Build rapid prototypes and proofs of concept to validate emerging technology, product opportunities and customer scenarios.

Establish the engineering practices these systems need: evaluation, testing, observability, monitoring, governance, security and operational excellence.

Contribute to technical design, review other engineers’ work, and support colleagues coming into the domain.

Represent the work outside the team through customer engagements, demonstrations, industry events and partner collaboration.

Qualifications

Strong software engineering first. Everything else is applied on top of that.

Production experience building and operating enterprise systems, with real depth in distributed systems, cloud-native architectures, API and schema design, event-driven systems, security, observability and CI/CD.

Strong programming in a modern backend language. See the note on language under “Open calls” before advertising.

Experience delivering AI systems built on large language models, retrieval-augmented generation (RAG), agentic workflows and orchestration frameworks, including tool use, function calling, workflow orchestration and autonomous or multi-agent architectures, with the judgement to know where they fail.

Evaluation as a discipline: experimentation, benchmarking, prompt engineering, tracing, quality measurement and agent tuning, improving an agent against evidence rather than impression.

Ability to design solutions that integrate enterprise applications, business processes, workflows and data platforms.

Depth in at least one of the following:

Tool-surface and agent-runtime engineering. MCP servers, tool ecosystems, capability modelling, discoverability, governance, versioning, backward compatibility, multi-tenancy isolation.

Knowledge graphs and semantic modelling. Ontology design, context engineering, embeddings, vector databases, retrieval and search technologies, memory architectures, grounding strategies.

Enterprise platform depth. Oracle PL/SQL, OData, and comfort working inside large metadata-driven systems where behaviour is configured rather than coded.

Desirable

Experience with agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangGraph, AutoGen, PydanticAI, the OpenAI Agents SDK or CrewAI.

Experience building reusable AI platforms, MCP ecosystems or shared engineering capabilities used across multiple products and teams.

Containerised platforms and infrastructure automation: Docker, Kubernetes.

Experience with Azure, AWS, GCP or another hyperscale cloud platform.

Reverse-engineering or interpreter work.

Token-efficient agent design.

Enterprise software domains: enterprise asset management, service management, manufacturing, supply chain, aerospace and defence, energy, telecommunications, construction, industrial AI.

Contributions to open-source projects, technical communities, conferences, publications or standards.

Additional Information

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.