AI Engineer
Role details
Job location
Tech stack
Job description
Judicial Transcribe is an AI-enabled platform designed to deliver automated transcription and AI-assisted processing of judicial audio recordings. The service is already deployed at scale and is now entering the next phase of operational maturity.
The current focus is to stabilise the live service, strengthen engineering resilience, and extend capabilities including transcription from uploaded audio files, anonymisation/redaction, and AI-driven summarisation.
The programme also acts as a testbed for evolving AI engineering practices , helping to establish reusable patterns for future AI-enabled products and services.
To support this phase, the team is seeking two experienced AI Engineers for an initial six-month engagement.
Role Focus
The AI Engineer will focus on AI capability development and production maturity, helping to improve reliability, scalability, and engineering practices across the service.
You will work closely with engineers, product teams, and delivery leads to expand core functionality while ensuring robust, secure, and well-governed AI workflows., AI Service Development
- Support and enhance the live Transcribe service, focusing on AI components.
- Design and implement transcription workflows for uploaded audio files.
- Develop anonymisation and redaction capabilities aligned with publication requirements.
AI Integration & Capabilities
- Support the transcript summarisation proof of value (PoV).
- Enable reuse of existing summarisation capabilities through API-based integration.
- Work with internal API platforms to expose and consume AI capabilities.
AI Engineering Practices
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Strengthen AI pipeline patterns including:
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Extract - Chunk - Prompt - Evaluate workflows
Integrate evaluation mechanisms and Responsible AI controls into AI features.
Contribute to the safe and structured use of AI coding assistants within the development life cycle.
DevSecOps & Operational Maturity
- Improve AI-specific CI/CD practices, testing frameworks, and deployment processes.
- Enhance observability, monitoring, and production readiness for AI services.
- Support improvements in system resilience and operational stability.
Knowledge Sharing & Engineering Resilience
- Reduce single-point-of-failure risks by documenting systems and sharing knowledge.
- Contribute to engineering standards, documentation, and operational playbooks.
Expected Outcomes
During the six-month engagement the team aims to deliver:
- Stable and sustainable live support capability for the Transcribe platform.
- Delivery of audio file transcription capability.
- Delivery or controlled rollout of anonymisation/redaction features.
- Clear integration Pathway for transcript summarisation services via API.
- Improved deployment confidence and release cadence.
- Reduced dependency on individual engineers through improved documentation and shared knowledge.
- Strengthened AI engineering practices and operational maturity across the service
Requirements
Core Technical Skills
- Strong experience building AI-enabled applications in production environments.
- Experience with speech-to-text/transcription pipelines or similar AI workloads.
- Hands-on experience with LLMs, prompt engineering, and evaluation workflows.
- Experience designing AI pipelines and data processing workflows.
Engineering & DevOps
- Experience with CI/CD pipelines and DevSecOps practices.
- Knowledge of API integration and microservice architectures.
- Experience implementing observability and monitoring for AI systems.
Additional Experience
- Familiarity with Responsible AI principles and governance frameworks.
- Experience integrating AI services via APIs or internal platforms.
- Strong documentation and knowledge-sharing practices.
Nice to Have
- Experience working on AI services within regulated or public-sector environments.
- Experience with audio processing or speech recognition technologies.
- Experience implementing AI evaluation frameworks.
