ML Engineer (Forward Deployed Engineering)
CompanyApplied Computing
Location
London, England, United Kingdom
Employment TypeFull-time
Posted At3/10/2026
UK Visa Sponsorship Analytics
Analytics are greyed out due to low classification confidence (33.0%).
Occupation Type
Metal working machine operatives
Occupation Code Skill LevelIneligible
Sponsorship EligibilityIneligible for Sponsorship
Above analytics are generated algorithmically based on job titles and may not always be the same as the company's job classification. You can also check detailed occupation eligibility, and salary criteria on our UK Visa Eligible Occupations & Salary Thresholds page.
Disclaimer: Hunt UK Visa Sponsors aggregates job listings from publicly available sources, such as search engines, to assist with your job hunting. We do not claim affiliation with Applied Computing. For the most up-to-date job details, please visit the official website by clicking "Apply Now."
Description
Job Description
Orbital is a physics-grounded AI copilot that operates complex industrial systems such as refineries, upstream assets, and energy-intensive plants. It combines realtime time-series forecasting, physics-based models, and domain-trained language models to deliver interpretable insights, anomaly detection, and optimisation
pathways directly to operations teams.
As a Forward Deployed ML Engineer, your job is to make Orbital’s AI systems work in customer reality. You will deploy, configure, tune, and operationalise our deep learning models inside live industrial environments; spanning cloud, on-premise, hybrid, and air-gapped infrastructure.
This is not a pure research role.
You are not training experimental models in isolation. You are adapting production AI systems to customer data, configuring agents and RAG pipelines, tuning anomaly detection, and ensuring models deliver value in production workflows.
If Research builds the models, you make them work on-site.
Operating Context
Forward Deployed ML Engineers operate in pods of three alongside:
Job Requirements
Orbital is a physics-grounded AI copilot that operates complex industrial systems such as refineries, upstream assets, and energy-intensive plants. It combines realtime time-series forecasting, physics-based models, and domain-trained language models to deliver interpretable insights, anomaly detection, and optimisation
pathways directly to operations teams.
As a Forward Deployed ML Engineer, your job is to make Orbital’s AI systems work in customer reality. You will deploy, configure, tune, and operationalise our deep learning models inside live industrial environments; spanning cloud, on-premise, hybrid, and air-gapped infrastructure.
This is not a pure research role.
You are not training experimental models in isolation. You are adapting production AI systems to customer data, configuring agents and RAG pipelines, tuning anomaly detection, and ensuring models deliver value in production workflows.
If Research builds the models, you make them work on-site.
Operating Context
Forward Deployed ML Engineers operate in pods of three alongside:
- Full Stack Engineers
- Data Engineers
Job Requirements
- MSc in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.
- Strong proficiency in Python and deep learning frameworks (PyTorch preferred).
- Solid software engineering background; designing and debugging distributed systems.
- Experience building and running Dockerised microservices, ideally with Kubernetes/EKS.
- LLM API integrations (OpenAI, Claude, Gemini), FastAPI for ML services and REST inference APIs
- Familiarity with message brokers (Kafka, RabbitMQ, or similar).
- AI systems are deployed and running in customer environments.
- Models are tuned to customer data and delivering operational value.
- Anomalies and predictions are trusted by engineers.
- Multi-agent copilots function reliably in production workflows.
- RAG systems retrieve accurate, domain-relevant insights.
- Inference pipelines run with high uptime and low latency.
- AI System Deployment & Configuration
- Deploy Orbital’s AI/ML services into customer environments.
- Configure inference pipelines across cloud, on-prem, and hybrid infrastructure.
- Package and deploy ML services via Docker/Kubernetes.
- Ensure inference services are reliable, scalable, and production-ready.
- Time Series & Predictive Model Tuning
- Deploy and tune time-series forecasting and anomaly detection models.
- Adapt models to customer-specific industrial processes.
- Configure thresholds, alerting logic, and detection sensitivity.
- Validate model outputs against engineering expectations.
- Gradient boosting models (LightGBM)
- Transformer models
- Statistical anomaly detection methods
- Multivariate monitoring systems