ML Engineer (Forward Deployed)

About the role

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.

Responsibilities

• Deploy, configure, tune, and operationalise our deep learning models inside live industrial environments.
• Adapt production AI systems to customer data, configuring agents and RAG pipelines.
• Tune anomaly detection to ensure models deliver value in production workflows.
• Ensure models are reliable, scalable, and production-ready.
• Deploy and tune time-series forecasting and anomaly detection models.
• Configure multi-agent AI systems for customer workflows.
• Deploy and configure RAG pipelines in customer environments.
• Deploy and configure SQL and visualization agents for structured and exploratory analytics.
• Generate SHAP explanations and build interpretability reports for model outputs.
• Support trust and adoption of AI insights by explaining anomaly drivers and optimisation recommendations.
• Deploy AI systems into restricted industrial networks, integrating inference pipelines with historians, OPC UA servers, IoT data streams, process control systems, and satisfying infrastructure and security constraints.
• Debug production issues in live operational environments.
• Monitor inference performance and drift, troubleshoot production model failures, and maintain containerised ML deployments.

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.
• Experience with LLM API integrations (OpenAI, Claude, Gemini), FastAPI for ML services and REST inference APIs.
• Familiarity with message brokers (Kafka, RabbitMQ, or similar).
• Familiarity with hybrid cloud/on-prem deployments (AWS, Databricks, or industrial environments).
• Exposure to time-series or industrial data (historians, IoT, SCADA/DCS logs) is a plus.
• Domain experience working as a data scientist in oil and gas or energy is a plus.
• Ability to work in forward-deployed settings, collaborating directly with customers.
• Comfortable in customer-facing technical roles.
• Able to operate in forward-deployed environments.
• Strong troubleshooting capability in production AI systems.

Qualifications

• Exposure to time-series or industrial data (historians, IoT, SCADA/DCS logs) is a plus.
• Domain experience working as a data scientist in oil and gas or energy is a plus.
• Ability to work in forward-deployed settings, collaborating directly with customers.
• Comfortable in customer-facing technical roles.
• Able to operate in forward-deployed environments.
• Strong troubleshooting capability in production AI systems.

Skills

• Python
• Deep learning frameworks (PyTorch preferred)
• Kubernetes/EKS
• LLM API integrations (OpenAI, Claude, Gemini)
• FastAPI for ML services and REST inference APIs
• Message brokers (Kafka, RabbitMQ, or similar)
• Hybrid cloud/on-prem deployments (AWS, Databricks, or industrial environments)
• Time-series or industrial data (historians, IoT, SCADA/DCS logs)
• Oil and gas or energy domain experience
• Forward-deployed settings
• Customer-facing technical roles
• Production AI systems troubleshooting

Benefits

• Opportunity to work on cutting-edge AI solutions for a mission-driven company.
• Work in a dynamic, fast-paced environment with a team of passionate professionals.
• Competitive compensation package including equity options.
• Flexible work arrangements to support a healthy work-life balance.
• Access to professional development opportunities and training programs.

Pay

Competitive salary commensurate with experience.

Schedule

Full-time position with flexible hours to accommodate customer needs.