Industrial AI Engineer

Alike Thoughts; India; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Alike Thoughts seeks an Industrial AI specialist to translate customer needs into predictive analytics for asset health. You will analyze P&IDs, data sources, and equipment behavior to build robust ML-based monitoring solutions.

Contribute to model validation, feature selection, and integration with industrial platforms, while collaborating with product, data science, and engineering teams to scale deployment across customers in India.

Qualifications

• Industrial domain experience across rotating or process equipment and failure modes.
• Knowledge of condition monitoring, RCA, FMEA, and equipment health.
• Understanding of AI/ML in industrial contexts including time-series analytics.
• Familiarity with industrial analytics platforms and data provenance.
• Ability to interpret P&IDs, PFDs, and equipment datasheets for modeling.
• Skill in mapping sensors and tags from DCS/SCADA to analytics models.
• Ability to validate model outputs against real operating data and events.
• Experience with analytics solutions for rotating machinery.
• Capability to bridge domain engineering and data science teams.

Responsibilities

• Translate customer business objectives into Industrial AI use cases.
• Analyze process and equipment data to define predictive analytics tasks.
• Define asset-to-tag relationships across data sources.
• Configure, deploy, and operationalize predictive models for equipment health.
• Interpret model outputs and assess operational relevance.
• Perform functional validation and QA of analytics models.
• Support development of AI/ML monitoring approaches with domain knowledge.
• Collaborate with Product, Project Management, Data Science, and Engineering teams.
• Identify opportunities to improve model effectiveness and deployment workflows.
• Develop reusable domain knowledge and best practices for analytics implementations.

Skills

Industrial domain

Predictive maintenance

AI/ML concepts

Analytics platforms

Engineering documentation

Tag mapping

Model validation

Analytics exposure

Cross-functional collaboration

Tools

GE SmartSignal

AVEVA PRiSM

Aspen Mtell

UptimeAI

Prometheus APM

Bently Nevada System1

Job description

Key Responsibilities

• Understand customer business and reliability objectives and translate them into Industrial AI / predictive analytics use cases.
• Analyze P&IDs, PFDs, equipment design, operating philosophy, and performance/efficiency calculations to understand asset processes, operating boundaries, and failure behavior.
• Define and validate asset-to-tag relationships, ensuring relevant process, mechanical, and performance parameters are correctly mapped from DCS, SCADA, historians, and other data sources.
• Work with AI/ML and predictive analytics platforms to configure, deploy, and operationalize models for equipment health and performance monitoring.
• Apply engineering domain knowledge to interpret model outputs, anomalies, patterns, correlations, and early warnings and assess their operational relevance.
• Perform functional validation and quality assurance of analytics models by comparing model behaviour with equipment operating conditions, historical patterns, and known failure mechanisms.
• Support the development of AI/ML-driven monitoring approaches by providing equipment-domain knowledge, failure-mode understanding, and relevant feature/tag selection.
• Collaborate with Product, Project Management, Data Science, Engineering, and Technical Consulting teams to ensure solutions meet customer requirements.
• Identify opportunities to improve model effectiveness, monitoring workflows, automation, and deployment scalability.
• Develop reusable domain knowledge, validation methodologies, equipment templates, and best practices across predictive analytics implementations.

Qualifications & Functional Skills

• Industrial Domain Experience: Strong understanding of rotating and/or process equipment, operating principles, performance characteristics, and common failure mechanisms.
• Predictive Maintenance & Reliability: Working knowledge of condition monitoring, predictive maintenance, RCA, FMEA, RCM, and equipment health assessment.
• AI/ML Familiarity: Good understanding of AI/ML concepts relevant to industrial applications, including anomaly detection, regression, classification, pattern recognition, time-series analytics, model training, validation, and performance evaluation.
• Predictive Analytics Platforms: Familiarity with industrial analytics/APM platforms such as GE SmartSignal, AVEVA PRiSM, Aspen Mtell, UptimeAI, Prometheus APM, Bently Nevada System1, or similar solutions.
• Engineering Documentation: Ability to interpret P&IDs, PFDs, equipment datasheets, control philosophy, process calculations, and performance curves.
• Tag Mapping & Data Understanding: Ability to identify and map relevant sensor/process tags from DCS, SCADA, PLCs, and historians to analytics models.
• Model Validation: Ability to evaluate whether model outputs and detected anomalies are technically meaningful and consistent with actual equipment/process behavior.
• AI/ML Implementation Exposure: 2+ years of experience working with analytics solutions, AI/ML-based monitoring, predictive models, or rule-based diagnostic solutions for rotating machines in industrial environments.
• Cross-functional Collaboration: Ability to bridge the gap between domain engineering, customer requirements, data science, and technology teams.