Gen AI Architect

Required Technical Skill Set 

• Industrial AI & Data Science Expertise
• Strong understanding of statistical modeling, time-series analysis, and signal processing for sensor and machine data.
• Experience in predictive maintenance, anomaly detection, and condition-based monitoring.
• Expertise in process optimization using AI/ML techniques (e.g., reinforcement learning, simulation-based optimization).
• Experience in computer vision for industrial quality inspection (e.g., defect detection, OCR, visual anomaly detection).
• Familiarity with industrial protocols and data ingestion from PLCs, SCADA, OPC UA, Modbus, MQTT, etc.
• Knowledge of digital twin modeling, simulation, and integration with real-time data.
• Generative AI & Advanced Architectures
• Application of GenAI for industrial use cases such as intelligent documentation, automated SOP generation, and conversational AI for technicians.
• Experience in building GenAI solutions using LLMs (OpenAI, vLLMs) on hyperscalers (Azure, AWS, GCP) and on-premise infrastructure.
• Development of Retrieval-Augmented Generation (RAG) pipelines and domain-specific knowledge graphs.
• Understanding of Agentic AI for orchestrating multi-agent systems in industrial workflows.

• Must Have-
• Proven experience in architecting AI/ML solutions for industrial domains (manufacturing/Smart Manufacturing, Industry 4.0, energy, utilities, etc.).
• Strong understanding of sensor data handling, time-series modeling, and edge deployment.
• Experience in predictive maintenance, process optimization, and quality inspection using AI.
• Hands-on experience with industrial data sources and protocols.
• Ability to deploy AI solutions on both hyperscalers and on-premise infrastructure.
• Strong Python programming skills and familiarity with ML/DL frameworks.
• Ability to lead architecture design and collaborate with OT/IT teams

Good to Have-

• Exposure to Agentic AI and multi-agent orchestration in industrial settings.
• Experience with digital twin platforms and simulation tools.
• Familiarity with industrial safety, compliance, and reliability standards.
• Certifications in AI/ML, cloud, or industrial automation.
• Knowledge of multimodal AI (vision + sensor fusion) for robotics or inspection.
• Experience with GenAI applications tailored to industrial documentation and training.