On-Site ML Engineer for Industrial AI Deployments

Applied Computing; Houston (TX); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Applied Computing is seeking a Forward Deployed ML Engineer to operationalise Orbital’s AI systems in live industrial environments across cloud, on-prem, and hybrid setups. You’ll deploy, configure, tune, and run production models, ensuring reliability and value in customer workflows.

You will work in small pods with Full Stack and Data Engineers, delivering 2–3 deployments per quarter, integrating RAG pipelines, anomaly detection, and multi-agent copilots to drive real-time insights.

Qualifications

• 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

Responsibilities

• AI System Deployment & Configuration: Deploy Orbital’s AI/ML services into customer environments.
• Time Series & Predictive Model Tuning: Deploy and tune time-series forecasting and anomaly detection models.
• Adapt models to customer-specific industrial processes and configure thresholds and alerting logic.

Skills

Python

PyTorch

LLM API integrations

FastAPI for ML services

Distributed systems design

Time-series data familiarity

Oil & gas domain experience

Forward-deployed collaboration

Customer-facing role

Production AI troubleshooting

Education

MSc in Computer Science, Machine Learning, Data Science, or related field

Tools

Docker/Kubernetes

Kubernetes/EKS

AWS

Databricks

Kafka

RabbitMQ

LLM APIs (OpenAI, Claude, Gemini)

Job description

Applied Computing is seeking a Forward Deployed ML Engineer to operationalise Orbital’s AI systems in live industrial environments across cloud, on-prem, and hybrid setups. You’ll deploy, configure, tune, and run production models, ensuring reliability and value in customer workflows.

You will work in small pods with Full Stack and Data Engineers, delivering 2–3 deployments per quarter, integrating RAG pipelines, anomaly detection, and multi-agent copilots to drive real-time insights.