Forward-Deployed ML Engineer: Scale Energy AI in Production

Applied Computing; Houston (TX); Hybrid; USD 120,000 - 170,000; Full time

Job summary

Applied Computing is hiring a Forward Deployed ML Engineer to deploy, configure, tune, and operationalise Orbital’s AI models in live industrial environments. You will work across cloud, on-premises, and hybrid infrastructures, integrating time-series forecasting, anomaly detection, and RAG pipelines with multi-agent configurations.

You will collaborate with full stack and data engineers to ensure production-ready, scalable AI services and direct customer impact in energy operations.

Qualifications

• MS/PhD-level education or equivalent practical experience in ML or data science.
• Strong Python and DL framework skills required.
• Experience deploying ML models in production environments.
• Familiarity with containerized services and microservices architecture.

Responsibilities

• 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.
• Tune models and pipelines for production performance.
• Collaborate with customers to integrate AI systems into workflows.

Skills

MSc in CS/ML/DS

Python & DL frameworks

Software engineering

Docker/Kubernetes

LLM integrations

Message brokers

Hybrid cloud/on-prem

Time-series data

Oil & gas domain knowledge

Forward deployment

Customer-facing

Production debugging

Education

MSc in Computer Science, ML, DS, or related field

Tools

Docker/Kubernetes

FastAPI

Kafka/RabbitMQ

AWS/Databricks

OPC UA / industrial protocols

Job description

Applied Computing is hiring a Forward Deployed ML Engineer to deploy, configure, tune, and operationalise Orbital’s AI models in live industrial environments. You will work across cloud, on-premises, and hybrid infrastructures, integrating time-series forecasting, anomaly detection, and RAG pipelines with multi-agent configurations.

You will collaborate with full stack and data engineers to ensure production-ready, scalable AI services and direct customer impact in energy operations.