Description

• 5-10years, Preferable: Production Engineering knowledge Oil & Gas industry
• Timeseries ML and constrained optimization for production ramp/sequence planning; ability to encode facility guardrails and drawdown targets.
• Pressuresystem feature engineering using DHP/WHP/Manifold/Up/Downstream signals; comfort reconciling telemetry with physical intuition.
• OT/historian (PI) data wrangling at scale; robust handling of gaps, sensor drift, and event slicing for ramp windows.
• Azure ML model packaging, endpoints, monitoring; handson with CI/CD for retrains and can migrate from HPCtrained models to cloudserved artifacts.
• Operatorcentric delivery: translating model outputs into clear ramp steps/visual cues (stoplights, countdowns) and validating against controlroom practice.

Qualifications:

• A highly skilled Machine Learning Engineer with 5–10 years of experience in timeseries forecasting, sensorlevel feature engineering, and optimization models for industrial/energy systems.
• Strong background working with PI historian, operational telemetry, and production facility constraints.
• Proficient in designing endtoend ML pipelines—from OT data extraction and feature engineering to model deployment, monitoring, and operatorcentric UI delivery.
• Adept at translating complex ML/optimization outputs into clear operational instructions used by field/production teams.
• Senior ML Engineer (6+ years)
• Applied Scientist – Energy Optimization
• OT Data + ML Specialist
• Azure ML: Pipelines, endpoints, environments, registries
• ADF and Azure Databricks (PySpark, Delta Lake, DLT)
• CI/CD via Azure DevOps — YAML pipelines, automated retrains
• Monitoring: Application Insights, Azure Monitor, data drift monitors
• Containerization (Docker), ONNX model packaging
• Working knowledge of containerization (Docker) and API deployment (FastAPI/Flask).

Preferred Qualifications

• Experience in oil & gas, energy, or industrial automation environments.
• Exposure to artificial lift systems (ESP/gaslift) or hydraulic flow models.
• Knowledge of physicsbased modeling, surrogate modeling, or hybrid ML+physics workflows.
• Experience with realtime streaming (Event Hubs, Kafka, IoT Hub).

Software Engineering Skills

• Python (NumPy, Pandas, PyTorch, Scikitlearn)
• PySpark, Delta Lake, ADLS
• REST APIs (FastAPI/Flask)
• Git, testing frameworks, logging & monitoring best practices

Key Skills

Education

Bachelor's degree