Senior ML Ops Engineer / Senior Machine Learning Ops Engineer
Fully Remote
£85,000 - £105,000 bonus healthcare
Technify Talent have partnered with a fast-growing technology company building AI-powered security systems. We're looking for a Senior ML Ops Engineer to join the team and own the training and inference infrastructure behind their production ML platform.
This is a bridge-between-research-and-production role: you'll take models built by the data science team and make them fast to train, dependable to deploy on edge hardware, and able to learn from real operational feedback.
What you'll do:
Model Training \& Scaling
- Build and own training infrastructure, from single-machine experiments to distributed, parallelised training at scale
- Optimise training pipelines for speed, cost, and reproducibility
- Establish standard training, evaluation, and experiment-tracking workflows (e.g. MLflow)
- Work with data scientists to turn promising research code into production-ready pipelines
Production Feedback Loops
- Design the feedback loop that captures inference outputs, operator decisions, and ground truth, routing them back into training and evaluation
- Build monitoring for model performance and data drift, with alerting on degradation
- Establish retraining pipelines so models improve on real operational data, not just synthetic data
Deployment \& MLOps
- Deploy and optimise models for inference on edge hardware (e.g. Jetson, DeepStream)
- Own MLOps tooling and CI/CD for models. Automated, versioned, repeatable deployment
- Optimise models for production using ONNX / TensorRT, balancing accuracy against latency and footprint
- Ensure deployed models are observable, versioned, and safe to roll back
What we're looking for:
- 5 years taking ML models from research into production in a professional setting
- Strong Python skills, with experience building production-grade ML pipelines/services
- Experience with distributed/parallelised model training
- Familiarity with PyTorch or TensorFlow
- Hands-on MLOps experience - experiment tracking, model versioning, CI/CD
- Experience deploying/optimising models for inference, including on edge hardware
- Production experience with a cloud platform (Azure preferred; AWS/GCP also welcome)
- Experience building monitoring/evaluation for models in production (performance, drift)
Nice to have: experience with Jetson, DeepStream, ONNX, or TensorRT; building production feedback/retraining loops; computer vision or sensor-data models.
Please apply directly to this advert if you'd like to be considered.
