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AI Engineer with less than a year in AI/ML & IoT.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Motivated AI/ML Engineer with foundational knowledge in AI, ML, Deep Learning, Data Analytics, and Intelligent Automation. Skilled in designing, developing, and deploying scalable AI solutions. Proficient in Python, TensorFlow, and PyTorch. Experienced in real-world applications involving CNNs and IoT projects. Seeking to leverage technical expertise and analytical skills to contribute to cutting-edge AI projects and drive business value in a dynamic environment.
Gokaraju Rangaraju Institute of Engineering and Technology
Bachelor of Technology
August 18, 2024 – Present
Govt Polytechnic Warangal
Diploma
September 1, 2021 – June 30, 2024
Sri Bhadrakali Technology Pvt. Ltd
Industrial Trainee | KU WGL
May 1, 2023 – December 31, 2023
India
Hand Gesture Recognition System
June 25, 2026 – Present
Designed and developed a CNN-based Hand Gesture Recognition System achieving high-accuracy gesture classification for real-time interaction. Integrated OpenCV-powered real-time video processing to enhance gesture detection speed, improve responsiveness, and optimize overall system performance.
Cultural Fit Analysis
The candidate's project and experience demonstrate a clear interest and focus on AI/ML, which aligns well with an AI Engineer role. The mention of team collaboration and Agile in their experience suggests a willingness to work in structured, team-oriented environments. The diversity of projects is limited to one detailed AI project, which is a minor area for growth in demonstrating broader applicability.
Soft Skills & Operational Fit
The candidate's resume highlights problem-solving, communication, team collaboration, and analytical thinking. The industrial trainee experience in an Agile environment suggests an operational fit for collaborative and iterative development processes. However, without specific assessment data, the depth of these soft skills cannot be fully validated.