AI Engineer (Manufacturing Solutions)

Company Description: Skillnet Learning specializes in Industry 4.0 and Digital Manufacturing skills development, helping organizations build future-ready talent for the manufacturing sector. The company collaborates closely with government skill bodies, skill councils, corporations, and public departments to align training with national and industry priorities. Skillnet Learning focuses on practical, industry-relevant programs that support the adoption of advanced technologies on the factory floor. Team members contribute to impactful initiatives that enhance workforce capabilities and support digital transformation in manufacturing.

Role Description: The AI Engineer (Manufacturing Solutions) will design, develop, and deploy AI-driven applications and tools tailored to digital manufacturing and Industry 4.0 use cases. Day-to-day responsibilities include building and training models for pattern recognition and predictive analytics, integrating AI components into existing software systems, and collaborating with engineering and manufacturing stakeholders to translate operational challenges into technical requirements. The role involves experimenting with neural networks and NLP techniques, optimizing solutions for performance and scalability, and documenting architectures, workflows, and best practices for internal and external use. This is a full-time, on-site position based on Zirakpur location, working closely with cross-functional teams to deliver practical AI solutions that improve efficiency, quality, and decision-making on the shop floor.

Key Responsibilities: AI Solutions Development: • Design and deploy AI/ML solutions for workforce productivity, inspection, and safety • Develop end-to-end AI pipelines from data collection to deployment • Implement edge and cloud-based AI inference • Integrate AI with cameras, IoT devices, and industrial systems

AI-Based Inspection & Safety: • Develop computer vision models for defect detection and PPE compliance • Support PoC, pilot, and scale-up deployments • Handle shopfloor challenges like lighting, vibration, and dust

Workforce Productivity Analytics: • Analyze operator productivity and cycle times • Identify bottlenecks and performance gaps • Present insights via dashboards

MSME Awareness & Outreach: • Conduct awareness programs and demos on AI & IoT in manufacturing • Develop case studies and reference architectures

Digital Readiness & Feasibility: • Conduct digital readiness assessments • Identify high-impact AI & IoT use cases • Prepare feasibility, ROI, and implementation roadmap reports

Documentation & Collaboration: • Prepare technical documentation and proposals • Work with OEMs, integrators, and cloud partners

Required Skills: • AI/ML fundamentals • Computer Vision (OpenCV, YOLO, CNNs) • Python, TensorFlow/PyTorch • Edge AI platforms (Jetson/OpenVINO preferred) • IoT and industrial data basics • Cloud AI platforms

Soft Skills: • Strong communication and problem-solving • Ability to explain AI to non-technical stakeholders • Willingness to travel

Qualification: B.E./B.Tech / M.Tech in Computer Science, AI/ML, Electronics, Mechatronics, or Mechanical Engineering

Experience :

2–7 years in AI/ML or manufacturing technology

• Strong foundation in Computer Science, including algorithms, data structures, and software engineering principles.
• Practical experience with Pattern Recognition and Neural Networks for classification, prediction, or anomaly detection.
• Hands-on Software Development skills using modern programming languages and frameworks relevant to AI solutions.
• Knowledge of Natural Language Processing (NLP) techniques and tools for building text-based or conversational applications.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and data processing pipelines.
• Familiarity with manufacturing processes, industrial data (e.g., sensor, PLC, MES/ERP), or Industry 4.0 concepts is highly beneficial.
• Ability to collaborate with multidisciplinary teams, communicate technical concepts clearly, and document solutions effectively.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical discipline, or equivalent practical experience.