Senior MLOps & Computer Vision Architect | Contract.

🎯 What You’ll Own (End-to-End)

1. MLOps & Azure Architecture

You’ll architect and build our entire cloud-native MLOps platform on Azure, including:

• High-throughput video processing infrastructure
• Robust data pipelines (Encord → Azure → Roboflow)
• Automated model training + versioning
• Deployment of high-performance inference services
• ETL pipelines to turn predictions into client-ready metrics

This is full system ownership — and you’ll be the one building it.

2. Computer Vision Strategy & Model Lifecycle

You will define how we track brand assets across sports video and social media content by:

• Designing the CV approach (models, data strategy, optimisation)
• Integrating Encord annotations with Roboflow training pipelines
• Deploying and optimising the model at scale
• Setting up monitoring to catch model drift before it becomes a problem

3. Human-in-the-Loop (HITL) Tooling

You’ll build the analyst verification interface that:

• Flags low-confidence detections
• Lets analysts quickly confirm/correct outputs
• Feeds improved data straight back into the training pipeline

This is the backbone of our continuous learning loop.

4. Knowledge Transfer

At the end of the project you’ll hand over:

• Documentation
• Architecture diagrams
• Runbooks
• Codebase walkthroughs

So our internal team can take full ownership.

🌟  Why This Role Is Special

• True ownership: You design it, you build it, you ship it.
• No legacy tech: Clean slate. You choose the right tools and architecture.
• Real-world impact: Your system will power visibility metrics used by global brands.
• Massive scope: MLOps, CV, cloud architecture, inference, ETL, HITL — all yours.
• Fast-moving domain: Sports + AI + video = one of the most exciting fields right now.

If you’re tired of being one engineer on a huge team and want to  own the entire machine , this is your chance.

🔧 What We’re Looking For

You don’t need every keyword — but you should have strong experience with:

• Building production MLOps systems on  Azure
• Designing data pipelines and automated model lifecycles
• Deploying and optimising  computer vision models (YOLO, Detectron, etc.)
• Working with annotation tools (Encord, Labelbox, Roboflow, etc.)
• Creating scalable inference systems for video/image workloads
• Building HITL or internal analyst tooling
• Shipping production systems in fast-paced environments

Contract Details

• 6-month contract (likely extension)
• Competitive day rate
• Hybrid initially then possible remote UK
• Start date: Jan 2026