1. Official ambassador and representative of Arrelic in your college/university
2. Coordinate the execution of activities (like a seminar, workshops, and training) in your college
3. Maintain relevant databases and submit monthly reports
4. Come up with innovative ideas on how we can associate with your college/university
5. Publicize our activities by Digital marketing (managing college specific FB page, emails, WhatsApp, posters, etc.)
1. can start the part time job/internship between 2nd Apr'20 and 24th May'20
2. are available for duration of 6 months
3. have relevant skills and interests
Added requirements
1. Students having strong leadership and immense love for competitive activities are most welcome to be a part of our extended family in their college/university
2. The candidate must be a registered candidate at a college/university
3. Strong interpersonal and vocational skills
4. A market enthusiast student with focused and career-oriented
5. Should be proactive, creative and enthusiastic
Rewards and incentives
1. Get exclusive Arrelic goodies and merchandise on becoming a campus ambassador
2. Certificate and letter of recommendation on successful completion of program
3. Top performers will get paid internships.
4. Can attend any paid workshop/training of Arrelic free of cost* throughout the year
5. LinkedIn recommendation to all eligible candidates after completion of the program
Arrelic is a fast-growing deep technology firm aiming to bring the next level of IoT based sensor technology to transform the mode of manufacturing operation and maintenance practice of various industries with extensive expertise in reliability engineering, predictive maintenance, industrial internet of things (IIoT) sensors, machine learning and artificial intelligence.
We provide a single ecosystem for catering all industry needs from consulting to IoT and analytics as well as providing training and development courses for different stakeholders.
We aim to help manufacturing industries to improve their overall plant productivity, reliability and minimize total production cost by 25-30% by eliminating machine downtime, lightening management decisions by analyzing the machine data with right mind and expertise; for worry-free operation.
