ML Engineer, CloudlyPulse

Cloudly Inc; India; On-site; INR 1,200,000 - 1,600,000; Full time

Benefits offered by this job

Competitive salary, negotiable based on experience

Performance-based commission structure

Two annual festive bonuses

Fully subsidized lunch

Real exposure to global enterprise AI deals

Job summary

Cloudly Inc is looking for a Machine Learning Engineer to develop ML models for predictive maintenance and anomaly detection. You will work with operational data from industrial environments to provide insights for plant operators, ensuring reliability in critical decision-making.

The ideal candidate has 2-4 years of experience in production ML, strong proficiency in Python, and familiarity with industrial IoT contexts. Attractive benefits include competitive salary and performance-based bonuses.

Qualifications

• 2 to 4 years of hands-on experience building and deploying ML models.
• Comfort with messy, heterogeneous real-world data.
• Ability to build trustworthy ML outputs for non-data-scientists.

Responsibilities

• Design and build ML models for anomaly detection and predictive maintenance.
• Develop and maintain time series analysis pipelines.
• Collaborate with platform team to integrate ML outputs.

Skills

Python

ML frameworks like PyTorch, scikit-learn, TensorFlow

Time series modeling

Anomaly detection

Understanding of edge deployment constraints

Building explainable ML outputs

Education

Bachelor's or Master's in Computer Science, Mechanical, Electrical Engineering, or Machine Learning

Tools

PLC systems

SCADA systems

Industrial IoT

Job description

As ML Engineer for CloudlyPulse, you will build the intelligence layer on top of the platform's real-time operational data. Industrial environments generate rich, continuous telemetry from pressures, temperatures, currents, equipment states, and process flows. Your job is to turn that data into predictive insight: detecting anomalies before they become failures, identifying patterns that indicate degrading equipment health, and building the ML capabilities that take CloudlyPulse from operational visibility to operational intelligence. This is a challenging ML environment. You will work with noisy, heterogeneous data from mixed‑era, mixed‑vendor equipment that was never designed to be ML‑friendly. You will build models that must be reliable enough to be trusted by plant operators making safety‑critical decisions at the edge.

ABOUT CLOUDLYPULSE

CloudlyPulse is an edge‑first operational system built specifically for brownfield industrial environments. It unifies real‑time monitoring, deterministic control, alarm management, and configuration diagnostics in a single coherent interface, running close to the PLC and working with the equipment already installed on the plant floor, without replacing existing control logic or disrupting operations. CloudlyPulse makes industrial operations intelligible: giving operators clear visibility into what the system is doing, why it is doing it, and what to do when something goes wrong.

JOB REQUIREMENT

• Design and build ML models for anomaly detection, predictive maintenance, and equipment health monitoring using PLC data streams and process telemetry from industrial environments
• Develop and maintain time series analysis pipelines that process continuous sensor data including pressures, temperatures, currents, and capacity readings
• Build models that identify abnormal operating conditions and emerging failure modes, with output confidence levels and explainability appropriate for plant operators
• Work with edge deployment constraints to build models that run reliably close to the PLC in resource‑constrained, mixed‑era environments
• Develop diagnostic intelligence capabilities that help operators identify root causes of equipment deviations faster than current manual processes
• Collaborate with the CloudlyPulse platform team to integrate ML outputs into the monitoring interface in ways that are immediately actionable for operators
• Build and maintain data collection and labeling pipelines for industrial equipment data, working with CloudlyMELT where relevant
• Validate model performance against real operational outcomes and maintain rigorous evaluation standards appropriate for safety‑relevant industrial systems
• Monitor model behavior in production and implement retraining and drift handling strategies appropriate for evolving industrial environments

YOU MAY BE A GOOD FIT IF YOU HAVE

• 2 to 4 years of hands‑on experience building and deploying ML models in production environments
• Strong proficiency in Python and ML frameworks such as PyTorch, scikit‑learn, or TensorFlow
• Experience with time series modeling, anomaly detection, or predictive maintenance applications
• Comfort working with messy, heterogeneous real‑world data from physical systems
• Understanding of edge deployment constraints and experience optimizing models for inference in resource‑limited environments
• Ability to build explainable, trustworthy ML outputs appropriate for non‑data‑scientist end users
• Genuine curiosity about industrial systems and how AI can make them safer and more reliable

PREFERRED QUALIFICATIONS

• Experience with industrial IoT, SCADA systems, OT data, or manufacturing process data
• Familiarity with PLC systems, sensor protocols, or industrial communication standards such as Modbus or OPC‑UA
• Experience with edge ML frameworks or on‑device inference optimization
• Knowledge of fault detection and classification in physical systems
• Bachelor's or Master's degree in Computer Science, Mechanical or Electrical Engineering, Machine Learning, or a related field

COMPENSATION & BENEFITS

• Salary: Competitive base, negotiable based on experience
• Performance‑based commission structure: your earnings scale directly with your results
• Two annual festive bonuses, each equivalent to half a month's salary
• Two‑day weekends, 10 days casual leave, 10 days sick leave, and 14 public holidays per CloudlyIO's global holiday calendar
• Fully subsidized lunch and evening snacks, plus tea and coffee throughout the day
• Direct collaboration with US clients and teams, with real exposure to global enterprise AI deals from day one