Software Engineer

Job Purpose
The Senior Software Engineer owns the application and integration layer of Croner’s generative AI solutions: MCP integration, agent-to-agent connectivity, API design and service boundaries, together with the production front end through which customers and colleagues use these capabilities. It is a full-stack role: the same engineer designs the service contracts and builds the interface that consumes them.

As part of Croner’s AI team, the role exists to give the application and integration layer a dedicated owner. The AI engineers own retrieval, model behaviour and evaluation; the integration, API and interface work that currently sits alongside those responsibilities moves to this role, which establishes clear contracts and boundaries between the AI layer, the platform, and client applications. The role works closely with the AI Platform Engineer—who owns infrastructure, runtime and operational concerns—consuming the platform they provide rather than owning it.

The front-end side of the role is delivered in close collaboration with the dedicated AI UX designer being recruited by the UX team, turning their designs into accessible, responsive production interfaces that handle the particular demands of generative AI: streaming output, citations, agent progress, and graceful failure. The role also provides hands-on technical support to other engineers and helps shape the engineering standards of the wider AI team.

Objectives

• Own the application and integration layer of Croner’s AI products end to end: integrations, APIs, service boundaries, and the production front end.

• Build and maintain MCP servers and agent-to-agent connectivity that give AI capabilities safe, well-governed access to Croner’s systems and content.

• Design clear API contracts and service boundaries so that AI logic, platform, and interface concerns remain separable and independently deliverable.

• Deliver a production-grade front end for Croner’s AI products, working to designs produced by the AI UX designer.

• Take ownership of the integration, API and interface work that currently sits with the AI engineers, so that they can focus on retrieval, model behaviour and evaluation.

• Set and uphold engineering standards for the software layer, and provide technical support and mentoring to colleagues working within it.

Key Responsibilities
MCP & Agent Connectivity

• Design, build and maintain MCP servers that expose Croner’s systems, content and tooling to AI agents, with clear contracts, authentication and permissioning.

• Implement agent-to-agent connectivity, including protocol choices, message contracts, and the handling of state, retries and failure modes.

• Own versioning, backwards compatibility and documentation of the integration surface so that consumers can depend on it with confidence.

API Design & Service Boundaries

• Design and implement the APIs through which AI capabilities are consumed, with attention to contract clarity, error semantics, idempotency and observability.

• Define and maintain the service boundaries between AI logic, platform services and client applications, keeping responsibilities in the right place.

• Integrate with Croner’s wider systems and third-party services, handling authentication, rate limiting, and data-handling requirements.

Front-End Engineering

• Build and maintain the production front end for Croner’s AI products in React and TypeScript.

• Work in close collaboration with the dedicated AI UX designer to turn designs into accessible, responsive and well-tested interfaces.

• Implement the interaction patterns generative AI demands, including streaming responses, citation and source display, agent or tool-call progress, error and fallback states, and user feedback capture.

Quality, Testing & Performance

• Establish and maintain automated testing across the application and integration layer, from unit and contract tests through to end-to-end coverage of key user journeys.

• Instrument the layer for observability, working with the AI Platform Engineer so that logging, tracing and metrics fit the wider platform.

• Monitor and improve the latency, responsiveness and reliability of the paths users experience directly.

Technical Leadership & Team Development

• Act as technical owner of the application and integration layer, making and documenting the design decisions it requires.

• Provide hands-on technical support and mentoring to engineers working in this layer, including code reviews and design walkthroughs.

• Establish and promote best practices for API design, front-end engineering, testing and documentation.

Collaboration

• Work closely with the Director of Search & Generative AI on the technical direction of the software layer.

• Partner with the Senior AI Engineer and AI Engineers so that integration and interface concerns are handled in this layer rather than inside the AI codebase.

• Partner with the AI Platform Engineer on deployment, environments and operational readiness.

• Collaborate with the AI UX designer, product and delivery roles to clarify requirements and support delivery.

Skills & Requirements

Technical Skills

• Strong proficiency in Python for production-grade back-end and integration development.

• Strong proficiency in TypeScript /Javascript and React JS for production front-end development.

• Demonstrable strength in API design, including REST and/or GraphQL, streaming interfaces such as SSE or WebSockets, versioning, authentication and authorisation.

• Hands-on experience with the Model Context Protocol (MCP) or comparable tool and plugin integration protocols, and a clear interest in agent-to-agent connectivity patterns.

• Solid working knowledge of AWS — for example Lambda, ECS, API Gateway, S3, CloudFront, Cognito and IAM — sufficient to build and run services on the platform provided by the AI Platform Engineer.

• Experience designing and operating distributed, event-driven systems with strong observability practices, and working knowledge of data stores and platforms such as MongoDB, OpenSearch and Databricks; familiarity with Kubernetes is a plus.

• Strong testing discipline across back end and front end, and fluency with code versioning and CI/CD pipelines.

• Working understanding of accessibility standards and front-end performance.

• Familiarity with generative AI application patterns such as RAG, agents and tool use is advantageous; deep AI or ML expertise is not required.

Experience

• Demonstrable experience delivering complex software in professional environments, showing sustained depth of expertise and progression in responsibility.

• Demonstrable experience building, deploying and operating production-grade, customer-facing or business-critical applications across both API and interface layers.

• Demonstrable experience acting as a technical lead or system owner for complex platforms, with responsibility for technical design decisions, implementation quality and delivery outcomes.

• Demonstrable experience integrating systems across internal and third-party boundaries, including considerations of security, performance, scalability and reliability.

• Demonstrable experience working alongside designers to deliver production interfaces.

Behavioural Skills

• Experience operating as a senior software engineer or technical lead within an engineering team.

• Strong problem-solving skills and attention to implementation quality.

• Comfortable working full-stack, moving between back-end integration work and front-end delivery, and willing to go deep in both.

• A clear sense of where responsibilities belong, with the pragmatism to hold service boundaries without slowing delivery.

• Ability to balance experimentation with delivery.

• Clear and effective communicator within technical and cross-functional teams.