Senior Technical Lead

Job Summary

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.

Key Responsibilities

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).

Skill Requirements

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 (Num Py, Pandas, Py Torch, Scikitlearn)

Py Spark, 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

Other Requirements

null