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AI Engineer with less than a year in ML & Fullstack Development
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Nikhil Kumar Jha is an aspiring AI Engineer currently pursuing a B.Tech in Computer Science and Engineering with a specialization in AI and Machine Learning. With 3 months of experience in an AI-ML Virtual Internship, Nikhil has developed end-to-end ML pipelines, managed project codebases with Git, and earned AICTE certification. His project portfolio demonstrates expertise in MERN stack development, AI-powered applications, and CI/CD implementation, leveraging skills in Python, JavaScript, and various ML/web frameworks.
Noida Institute of Engineering and Technology
B.Tech · Computer Science and Engineering (Artificial Intelligence and Machine Learning)
August 1, 2022 – June 30, 2026
Eduskills Foundation
AI-ML Virtual Internship
September 1, 2023 – November 1, 2023
India
Workout Recommendation System
June 1, 2026 – Present
• Architected and deployed a full-stack MERN fitness platform using Git feature-branch workflow with pull-request reviews, maintaining clean release history across 3 version milestones. • Implemented a CI/CD pipeline using GitHub Actions to automate linting, testing, and deployment on push to main — reducing manual release steps by 70%. • Built RESTful APIs for handling user data and workout logic with scalable backend architecture. • Designed and exposed RESTful APIs (10+ endpoints) for user data and workout logic; documented APIs with Postman collections for team collaboration. • Integrated Recharts for real-time progress tracking dashboards and built responsive UI with React and Tailwind CSS, achieving cross-device compatibility.
AI Travel Itinerary Generator
June 1, 2026 – Present
• Developed and released an AI-powered travel planning application using MERN stack + Gemini API; maintained codebase with Git branching strategy (feature → develop → main). • Configured application release pipeline: version tagging, changelog maintenance, and staged rollout to ensure zero-downtime deployments. • Designed REST APIs to manage user preferences and real-time travel data, applying application lifecycle management principles from requirements → build → release. • Optimized frontend state management and API call efficiency, reducing average response time by 20% through memoization and request debouncing.
AWS Academy Certified: Cloud Foundations, Machine Learning Foundations
AWS Academy
June 1, 2026 – Present
Microsoft Azure AI Certified: Document Intelligence, Vision Solution
Microsoft Azure AI
June 1, 2026 – Present
Coursera Certified: Getting started with AI using IBM Watson
Coursera
June 1, 2026 – Present
Infosys: React JS, Spring and Angular
Infosys
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (travel, fitness) and the AI-ML virtual internship show a broad interest in applying AI/ML across different domains. The use of various technologies (MERN stack, Python ML libraries, cloud platforms) indicates adaptability and a willingness to learn. The target role of 'AI Engineer' aligns well with their educational background in AI/ML and their practical project experience. However, the candidate is still early in their career, which might impact their immediate cultural fit in a senior role requiring extensive industry experience.
Soft Skills & Operational Fit
The candidate demonstrates good operational fit through their experience with CI/CD pipelines, Git branching strategies, and application lifecycle management. Their project descriptions suggest an understanding of structured development processes. However, without specific psychometric or English test results, it's difficult to assess soft skills like logical reasoning, work attitude, stress handling, or team collaboration.