AI Engineer with 1+ years in Machine Learning & Gen AI
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated AI/ML Engineer Intern with 1.3 years of experience in developing and deploying intelligent systems. Proficient in Python, SQL, TensorFlow, PyTorch, and Gen AI frameworks. Demonstrated ability to architect event-driven microservices, design real-time digital twins, and implement RAG systems for complex data analysis, contributing to significant improvements in efficiency and performance.
Amity University
Bachelor of Technology · Artificial Intelligence & Machine Learning
August 1, 2023 – June 30, 2027
Container Terminal Automation Project
AI/ML Engineer Intern
December 1, 2025 – Present
Gurgaon, Haryana, India
HackTech Solutions
Data Scientist Intern
April 1, 2025 – July 31, 2025
India
Urban Environmental Analysis Project
Freelance ML Engineer
February 1, 2025 – June 30, 2025
India
ML
GDSC
June 1, 2026 – Present
Prompt Engineering in VertexAI
GDSC
June 1, 2026 – Present
Docker & Kubernetes
HackTech
June 1, 2026 – Present
IoT & Arduino
HackTech
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from container terminal automation to financial document analysis and urban environmental research, indicates a broad interest and adaptability to different problem domains. The involvement in GDSC and HackTech certifications suggests a proactive learning attitude and engagement with technical communities. The roles undertaken (AI/ML Engineer Intern, Data Scientist Intern, Freelance ML Engineer) align well with the responsibilities of an AI Engineer, demonstrating a clear career path and commitment to the field. The breadth of skills across languages, frameworks, data, backend, DevOps, and Gen AI also points to a versatile individual who can contribute to various aspects of an AI project.
Soft Skills & Operational Fit
The candidate's resume highlights experience in project-based work (internships, freelance), suggesting an ability to work within defined scopes and deliver tangible results. The descriptions indicate a problem-solving approach, particularly in architecting systems and engineering solutions. The mention of achieving specific performance targets (e.g., sub-100ms latency, <10ms query performance, 98%+ task completion) suggests a results-oriented mindset. However, without direct interview data, assessing collaboration, stress handling, or adaptability is not possible.