ML Engineer (Forward Deployed)

Role Overview

As a Forward Deployed ML Engineer, you will make Orbital’s AI systems work in customer reality by deploying, configuring, tuning, and operationalising deep learning models inside live industrial environments. You will adapt production AI systems to customer data, configure agents and RAG pipelines, and ensure models deliver value in production workflows across cloud, on-premise, hybrid, and air-gapped infrastructure.

Responsibilities

• Deploy Orbital’s AI/ML services into customer environments and configure inference pipelines.
• Package and deploy ML services via Docker and Kubernetes.
• Deploy and tune time-series forecasting and anomaly detection models.
• Configure multi-agent AI systems and set up LLM provider integrations.
• Deploy RAG pipelines, configuring knowledge graphs and vector databases.
• Configure SQL agents and visualization agents for structured datasets.
• Generate SHAP explanations and build interpretability reports.

Requirements

• MSc in Computer Science, Machine Learning, Data Science, or equivalent practical experience.
• Strong proficiency in Python and deep learning frameworks like PyTorch.
• Solid software engineering background in designing and debugging distributed systems.
• Experience building and running Dockerised microservices with Kubernetes/EKS.
• Experience with LLM API integrations (OpenAI, Claude, Gemini) and FastAPI.
• Familiarity with message brokers such as Kafka or RabbitMQ.
• Comfort working in hybrid cloud/on-prem deployments (AWS, Databricks).

Skills

• Python
• PyTorch
• Kubernetes
• LLM
• RAG