Lead ML Engineer (Document AI NLP, Contract)

Intelance; Remote; GBP 80,000 - 100,000; Part time

Job summary

A specialist architecture and AI consultancy is seeking a Lead ML Engineer to develop an AI-assisted clinical tool for human genetic testing. In this part-time contract role, you will design and implement the core ML/NLP infrastructure, with responsibilities that include document processing and model evaluation. Ideal candidates will have strong Python skills and a solid background in Machine Learning and NLP. The position is remote-friendly, requiring 2-3 days of work per week, with a competitive day rate and potential for extension based on project success.

Benefits

Real impact in healthcare AI

Flexible remote-first work

Competitive day rate

Qualifications

• 4 years of hands-on Machine Learning / NLP engineering experience.
• Strong Python skills with experience in modern ML / NLP stacks.
• Practical experience with document AI and text processing.
• Solid understanding of LLMs and prompt-based workflows.
• Experience building evaluation pipelines for model selection.

Responsibilities

• Design and implement the ML / NLP core of an AI‑assisted marking tool.
• Document work clearly including experiments and model choices.
• Support architect in designing explainability and audit.

Skills

Machine Learning

NLP

Python

OCR outputs

Document AI

Tools

PyTorch

TensorFlow

HuggingFace

spaCy

Job description

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
  • 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, drift and 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) 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 for quick decisions, minimal bureaucracy.
• Remote‑first flexible: work from anywhere compatible with UK business hours with a planned load of 2‑3 days per 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 there's a good match, let's set up a short call to walk through the project expectations and next steps.