Machine Learning Engineer

Orcapod Consulting Services; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Orcapod Consulting Services in Bengaluru seeks a highly skilled ML Engineer with 5-10 years of experience in timeseries forecasting and feature engineering for industrial energy systems. The role emphasizes PI historian access, operational telemetry, and production constraints, delivering end-to-end ML pipelines and operator-centric UI.

Expertise in Azure ML, CI/CD via YAML pipelines, and real-time monitoring is required, as is strong communication with production engineers and SMEs.

Qualifications

• 5-10 years of experience in ML engineering focusing on timeseries forecasting and optimization for energy/industrial domains.
• Strong background with PI historian, operational telemetry, and production facility constraints.
• Proficient in end-to-end ML pipelines from data extraction to deployment, monitoring, and operator-centric UI delivery.

Responsibilities

• Design end-to-end ML pipelines from OT data extraction to model deployment and monitoring.
• Translate complex ML optimization outputs into clear operational steps for field production teams.
• Lead design discussions and technical decisions, with strong cross-functional collaboration.

Skills

Python

NumPy

Pandas

PyTorch

scikit-learn

Docker

ONNX

FastAPI

Flask

Git

Tools

PySpark

Delta Lake

ADLS

Job description

Azure ML model packaging endpoints monitoring handson with CICD for retrains and can migrate from HPCtrained models to cloudserved artifacts

Operatorcentric delivery translating model outputs into clear ramp stepsvisual cues stoplights countdowns and validating against controlroom practice

Profile Requirement

A highly skilled Machine Learning Engineer with 5-10 years of experience in timeseries forecasting sensorlevel feature engineering and optimization models for industrialenergy systems Strong background working with PI historian operational telemetry and production facility constraints Proficient in designing endtoend ML pipelinesfrom OT data extraction and feature engineering to model deployment monitoring and operatorcentric UI delivery Adept at translating complex MLoptimization outputs into clear operational instructions used by fieldproduction teams

Senior ML Engineer 3 years

Applied Scientist Energy Optimization

OT Data ML Specialist

Azure ML Pipelines endpoints environments registries

ADF and Azure Databricks PySpark Delta Lake DLT

CICD 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 FastAPIFlask

Preferred Qualifications

• Experience in oil gas energy or industrial automation environments
• Exposure to artificial lift systems ESPgaslift or hydraulic flow models
• Knowledge of physicsbased modeling surrogate modeling or hybrid MLphysics 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 FastAPIFlask
• 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

Shape

Work timing 01.30-10.30PM

Skills

Mandatory Skills : MLOPS,Time Series Machine LearningAzure Machine Learning,MLOps & CI/CD,Data Engineering (ADF, Databricks, PySpark),Python & Machine Learning Frameworks,Model Deployment & APIs,Monitoring, Drift Detection & Observability