Singareni skill training helps youth get employment
112 candidates selected for jobs through Singareni RG–1 Skill Development Centre
Singareni Collieries' skill training program has benefited 112 youth from areas affected by coal mines, including 87 women, by providing them with employment opportunities. The first batch of 55 selected women recently departed from Godavarikhani to start their new roles in Hyderabad. Officials from the RG-I area of Singareni Collieries recently conducted interviews for 154 candidates at the Singareni GVTC Skill Development Centre in Ramagundam–I Area.
Among the 127 women candidates interviewed, 87 were chosen for employment at Foxconn in Hyderabad, while 27 men were selected for Solar Technician positions, with 25 of them receiving employment offers.
Established two years ago, the skill development centre has been offering training and employment opportunities to local youth. Over the past two years, it has facilitated employment for more than 500 candidates. The center is actively seeking to train individuals in market-demanding courses and connecting skill development with suitable job opportunities.
Last week, Foxconn held special recruitment interviews for women candidates at the GVTC Skill Development Centre, selecting 87 women to work in assembly sections for Apple EarPods and electronic components at Foxconn's manufacturing facility in Hyderabad. These industrial sector opportunities aim to help local women achieve greater financial independence.
RG-I Area General Manager D. Lalith Kumar stated that 25 candidates who underwent training in the Solar Technician course were selected for employment and will join L&T Company within 10 days. He emphasized that Singareni is dedicated to equipping youth from Singareni-affected areas with the necessary skills to secure employment. He advised the selected candidates to utilize the offered opportunities, maintain discipline at work, and strive for professional growth.
Written by urgent.news from The Hindu's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.