ML Engineer (Forward Deployed)

Location: Houston, United States; hybrid

Company

hirify.global is an AI company building Orbital, a physics-informed foundation model for energy operations across oil and gas, refineries, and petrochemicals.

What you will do

• Deploy and configure Orbital AI/ML services across cloud, on-premise, hybrid, and air-gapped customer infrastructure.
• Package and maintain containerised ML services with Docker and Kubernetes, ensuring scalable and reliable inference.
• Tune time-series forecasting, anomaly detection, gradient boosting, transformer, and multivariate monitoring models for customer-specific industrial processes.
• Configure multi-agent systems, LLM integrations, SQL and visualisation agents, and RAG pipelines for operational workflows.
• Integrate inference systems with historians, OPC UA servers, IoT streams, and process control systems while collaborating with IT/OT teams.
• Monitor model performance and drift, troubleshoot production failures, manage model and dataset versions, and support CI/CD for model updates.

Requirements

• MSc in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.
• Strong Python skills and experience with deep learning frameworks, preferably PyTorch.
• Solid software engineering experience designing and debugging distributed systems.
• Experience with Dockerised microservices, ideally Kubernetes/EKS, FastAPI, REST inference APIs, and message brokers such as Kafka or RabbitMQ.
• Experience working with hybrid cloud and on-premise deployments, including AWS, Databricks, or industrial environments.
• Ability to troubleshoot production AI systems and collaborate directly with customers in forward-deployed technical roles.

Nice to have

• Exposure to time-series or industrial data, including historians, IoT, SCADA, or DCS logs.
• Data science experience in oil and gas or the energy sector.

Culture & Benefits

• Work in three-person delivery pods with Full Stack Engineers and Data Engineers.
• Each pod owns multiple customer deployments, including AI configuration, model tuning, agent orchestration, and inference reliability.
• Work directly in live industrial environments and collaborate with customer engineering, IT, and OT stakeholders.
• Focus on deploying production AI systems that improve operational decision-making, safety, and carbon intensity.