Intelance is a specialist architecture and AI consultancy working with clients in regulated high-trust environments (healthcare pharma life sciences financial services). We are building a lean senior team to deliver an AI-assisted clinical tool for a UK-based organisation in human genetic testing. We are looking for a Lead ML Engineer who can turn messy real-world documents into reliable explainable model outputs. This is a contract / freelance role part-time (2-3 days/week) working closely with our AI Solution Architect and Data Engineer.
Tasks
- Design and implement the ML/NLP core of an AI-assisted marking tool that:
Ingests clinical-style reports (PDF/Word) via an OCR parsing pipeline
Extracts relevant content and features
Applies a hybrid scoring approach (rules LLM / transformer models)
Outputs scores rationales and confidence levels.
- Build and iterate prompting / few-shot setups and rule layers so that model behaviour is consistent predictable and easy to explain to assessors.
- Work with the Data Engineer to define and consume clean structured inputs from the OCR/pipeline (schemas validation checks logging).
- Implement evaluation pipelines: ground-truth comparisons error analysis per-criterion metrics and drift/robustness checks.
- Optimise models for accuracy stability and cost (latency token usage throughput) within agreed constraints.
- Support the architect and compliance lead in designing explainability and audit: what is logged what is shown to assessors and what evidence is retained for validation.
- Package models behind clean interfaces (e.g. Python services APIs batch jobs) so they can be integrated with the rest of the system.
- Participate in technical workshops with the client to walk through behaviour on real examples and collect feedback.
- Document your work clearly: experiments model choices prompt patterns known limitations and recommended operating boundaries.
Requirements
Must-have
- 4 years of hands-on Machine Learning / NLP engineering experience (not just research).
- Strong Python skills and experience with at least one modern ML/NLP stack (PyTorch TensorFlow HuggingFace spaCy etc.).
- Practical experience with document AI / text processing: PDFs OCR outputs long-form text classification or scoring of documents.
- Solid understanding of LLMs and prompt-based workflows (e.g. OpenAI/Azure OpenAI Anthropic or similar) and how to mix them with rules / traditional models.
- Experience building evaluation pipelines: test sets metrics error analysis and data-driven model selection.
- Comfort working in environments where explainability auditability and consistency matter more than bleeding-edge novelty.
- Ability to work independently in a small senior team take ownership of a problem and communicate clearly about trade-offs.
- Available for 2-3 days per week on a contract basis working largely remotely in UK or close European time zones.
Nice-to-have
- Prior work in healthcare life sciences clinical reporting or regulated industries.
- Experience with Azure (Azure ML Azure Functions Azure OpenAI blob storage) or other major cloud providers.
- Exposure to validation or quality frameworks (e.g. GxP ISO 15189 UKAS NHS IG).
- Familiarity with MLOps practices (versioning deployment monitoring) even at a lightweight level.
Benefits
- Real impact: build a production AI system that will support external quality assessment in human genetic testing.
- Lean senior team: work directly with an AI Solution Architect experienced Data Engineer and the leadership team quick decisions minimal bureaucracy.
- Remote-first flexible: work from anywhere compatible with UK business hours with a planned load of 2-3 days/week.
- Contract / freelance: competitive day rate with the potential to extend into further phases and additional schemes if the pilot is successful.
- Opportunity to help define reusable ML/NLP components that Intelance will deploy across multiple regulated AI projects.
We review every application personally. If theres a good match well set up a short call to walk through the project expectations and next steps.
Intelance is a specialist architecture and AI consultancy working with clients in regulated high-trust environments (healthcare pharma life sciences financial services). We are building a lean senior team to deliver an AI-assisted clinical tool for a UK-based organisation in human genetic testing. W...
Intelance is a specialist architecture and AI consultancy working with clients in regulated high-trust environments (healthcare pharma life sciences financial services). We are building a lean senior team to deliver an AI-assisted clinical tool for a UK-based organisation in human genetic testing. We are looking for a Lead ML Engineer who can turn messy real-world documents into reliable explainable model outputs. This is a contract / freelance role part-time (2-3 days/week) working closely with our AI Solution Architect and Data Engineer.
Tasks
- Design and implement the ML/NLP core of an AI-assisted marking tool that:
Ingests clinical-style reports (PDF/Word) via an OCR parsing pipeline
Extracts relevant content and features
Applies a hybrid scoring approach (rules LLM / transformer models)
Outputs scores rationales and confidence levels.
- Build and iterate prompting / few-shot setups and rule layers so that model behaviour is consistent predictable and easy to explain to assessors.
- Work with the Data Engineer to define and consume clean structured inputs from the OCR/pipeline (schemas validation checks logging).
- Implement evaluation pipelines: ground-truth comparisons error analysis per-criterion metrics and drift/robustness checks.
- Optimise models for accuracy stability and cost (latency token usage throughput) within agreed constraints.
- Support the architect and compliance lead in designing explainability and audit: what is logged what is shown to assessors and what evidence is retained for validation.
- Package models behind clean interfaces (e.g. Python services APIs batch jobs) so they can be integrated with the rest of the system.
- Participate in technical workshops with the client to walk through behaviour on real examples and collect feedback.
- Document your work clearly: experiments model choices prompt patterns known limitations and recommended operating boundaries.
Requirements
Must-have
- 4 years of hands-on Machine Learning / NLP engineering experience (not just research).
- Strong Python skills and experience with at least one modern ML/NLP stack (PyTorch TensorFlow HuggingFace spaCy etc.).
- Practical experience with document AI / text processing: PDFs OCR outputs long-form text classification or scoring of documents.
- Solid understanding of LLMs and prompt-based workflows (e.g. OpenAI/Azure OpenAI Anthropic or similar) and how to mix them with rules / traditional models.
- Experience building evaluation pipelines: test sets metrics error analysis and data-driven model selection.
- Comfort working in environments where explainability auditability and consistency matter more than bleeding-edge novelty.
- Ability to work independently in a small senior team take ownership of a problem and communicate clearly about trade-offs.
- Available for 2-3 days per week on a contract basis working largely remotely in UK or close European time zones.
Nice-to-have
- Prior work in healthcare life sciences clinical reporting or regulated industries.
- Experience with Azure (Azure ML Azure Functions Azure OpenAI blob storage) or other major cloud providers.
- Exposure to validation or quality frameworks (e.g. GxP ISO 15189 UKAS NHS IG).
- Familiarity with MLOps practices (versioning deployment monitoring) even at a lightweight level.
Benefits
- Real impact: build a production AI system that will support external quality assessment in human genetic testing.
- Lean senior team: work directly with an AI Solution Architect experienced Data Engineer and the leadership team quick decisions minimal bureaucracy.
- Remote-first flexible: work from anywhere compatible with UK business hours with a planned load of 2-3 days/week.
- Contract / freelance: competitive day rate with the potential to extend into further phases and additional schemes if the pilot is successful.
- Opportunity to help define reusable ML/NLP components that Intelance will deploy across multiple regulated AI projects.
We review every application personally. If theres a good match well set up a short call to walk through the project expectations and next steps.
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