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AI/ML Engineer

Join our thriving digital agency in Teesside and be part of an energetic team building a new predictive AI product from the ground up.

Position Details

Location

Teesside, UK

Employment Type

Full-time

Experience

Mid to Senior Level

Required Skills

Machine LearningNLPPythonData PipelinesMLOps

Prefer email? Send your CV and a short covering note to hello@mcd-uk.com, marking the role you're applying for.

Overview

MCD Systems is a Teesside-based digital consultancy building enterprise-grade platforms for clients in maritime, insurance and energy. We're expanding into predictive AI, developing new machine learning capability on top of an existing, live maritime workforce safety platform already in use across major international shipping fleets.

This is a new role reporting into MCD's engineering team, leading the build of predictive risk models, NLP-based safety report analysis and human-supervised competency assessment tooling. The initial project phase runs to March 2027, with the product intended to grow into an ongoing SaaS platform for maritime operators, expanding into offshore energy.

The Role

This is a genuine opportunity to take ownership of the AI/ML side of a live product build, rather than a research exercise or a proof of concept. You'll design and deploy predictive models that flag at-risk workforce patterns ahead of incidents, build NLP pipelines that extract structured intelligence from unstructured safety reports and near-miss data, and develop human-supervised competency assessment scoring that keeps a person in the loop rather than automating the decision away entirely.

You'll also help design the AI infrastructure underneath all of this: data ingestion pipelines, the model training environment, and the API layer that integrates model output back into the product. You'll work closely with an academic partner, a Teesside University Applied AI research group, on predictive model design, and with external data partners to understand the safety report formats and workforce data structures you'll be building against.

You'll agree model accuracy, fairness and user-acceptance thresholds directly with the technical team and external pilot partners, and you'll have a junior developer working alongside you on delivery, dashboards and testing.

Key Responsibilities

  • Designing and building predictive risk models to flag at-risk workforce patterns ahead of incidents.
  • Building NLP pipelines to extract structured intelligence from unstructured safety reports and near-miss data.
  • Developing human-supervised competency assessment scoring, keeping a human in the loop rather than fully automating decisions.
  • Defining and agreeing model accuracy, fairness and user-acceptance thresholds with the technical team and external pilot partners.
  • Designing the AI infrastructure: data ingestion pipelines, the model training environment and the model-integration API layer.
  • Collaborating with an academic partner, a Teesside University Applied AI research group, on predictive model design.
  • Working closely with external data partners to understand safety report formats and workforce data structures.

What You'll Bring

  • Strong hands-on experience building and deploying machine learning models in production, not just research or notebooks.
  • NLP experience: text classification, information extraction, or similar work with unstructured documents.
  • Confidence working with predictive or risk modelling techniques.
  • Experience designing data pipelines and ML infrastructure, including cloud training environments and APIs.
  • The ability to work independently in a small team, translating ambiguous safety-domain problems into technical specifications.

Bonus Skills

  • Experience in safety-critical, compliance-heavy or maritime/industrial domains.
  • Experience with human-in-the-loop or human-supervised ML systems.

What We Offer

At MCD Systems, we believe in fostering a supportive and innovative environment where creativity and collaboration thrive. You'll be building a genuinely new product on top of a platform that's already live with major international shipping fleets, not iterating on an internal tool nobody uses.

We're committed to the personal and professional growth of our team, with direct access to an academic research partner and real pilot users to validate your work against from day one.