Senior Technical Lead

Years of Experience: 5-10years, Work timing - 01:30 -10:30 PM 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.

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).

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). Shape Software Engineering Skills Python (NumPy, Pandas, PyTorch, Scikitlearn) PySpark, Delta Lake, ADLS REST APIs (FastAPI/Flask) Git, testing frameworks, logging & monitoring best practices Shape 🔹 Soft Skills Strong crossfunctional communication with production engineers, operators, SMEs High ownership & ability to simplify complex ML outputs Can work with ambiguity and evolving requirements Comfortable leading design discussions and technical decisions

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