ML Engineer (Forward Deployed)

• 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).

• Comfort working in 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

What Success Looks Like
• 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.

1) 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.
2) 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.
Typical model classes include:
• Gradient boosting models (LightGBM)
• Transformer models
• Statistical anomaly detection methods
• Multivariate monitoring systems
3) Multi-Agent & LLM System Configuration
• Deploy and configure multi-agent AI systems for customer workflows.
• Set up LLM provider integrations (OpenAI, Claude, Gemini).
• Configure agent routing and orchestration logic.
• Tune prompts and workflows for operational use cases.
4) Retrieval Augmented Generation (RAG)
• Deploy RAG pipelines in customer environments.
• Ingest customer documentation and operational knowledge.
• Configure knowledge graphs and vector databases.
• Tune retrieval pipelines for accuracy and latency.
5) Intelligent Data Agents
• Configure SQL agents for structured customer datasets.
• Deploy visualization agents for exploratory analytics.
• Adapt agents to customer schemas and naming conventions.
6) Explainability & Interpretability
• Generate SHAP explanations for model outputs.
• Build interpretability reports for engineering stakeholders.
• Explain anomaly drivers and optimisation recommendations.
• Support trust and adoption of AI insights.
7) Forward Deployment & Customer Integration
• Deploy AI systems into restricted industrial networks.
• Integrate inference pipelines with:
o Historians
o OPC UA servers
o IoT data streams
o Process control systems
• Work with IT/OT teams to satisfy infrastructure and security constraints.
• Debug production issues in live operational environments.
8) Production Reliability & MLOps
• Monitor inference performance and drift.
• Troubleshoot production model failures.
• Version models and datasets (DVC or equivalent).
• Maintain containerised ML deployments.
• Support CI/CD for model updates.