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AI Engineer with 3+ years in Generative AI, Machine Learning & Python
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Information Technology graduate (May 2023) with hands-on experience in Generative AI, machine learning, and AI-powered application development. Proficient in Python and common machine learning libraries (scikit-learn, TensorFlow). Practical exposure to Generative AI concepts including LangChain, RAG pipelines, prompt engineering, and OpenAI API. Familiarity with NLP, transformer-based models, GCP, Docker, CI/CD, Git, and responsible AI principles. Demonstrated ability to design, develop, and deploy AI-based solutions, participate in code reviews, and collaborate with cross-functional engineering and product teams. Eager to learn, grow, and contribute to real-world Generative AI solutions at EY.
AISSMS Institute of Information Technology
Bachelor of Engineering · Information Technology
August 1, 2019 – May 1, 2023
AI-Powered Generative AI Application Platform
May 1, 2023 – June 1, 2026
Designed and developed a scalable end-to-end data ingestion, transformation, and analytics pipeline on GCP BigQuery, AWS, and Snowflake using Python, PySpark, SQL, and Docker; configured EKS, IAM, VPC, and S3 for secure, production-grade cloud infrastructure. Supported deployment of the Generative AI model into production using Docker and CI/CD pipelines; monitored AI system performance, accuracy, and reliability; followed best practices for model development, testing, and documentation. Participated in code reviews and collaborated with cross-functional engineering and product teams; applied emerging Generative AI tools and techniques to continuously improve solution quality; maintained technical documentation for all AI application components.
View ProjectSmart Product Recommendation Engine
May 1, 2023 – June 1, 2026
Designed and developed an AI-powered product recommendation system using collaborative filtering, content-based filtering, and hybrid ML approaches with scikit-learn and Python; applied machine learning model development best practices including training, validation, testing, and production deployment. Applied NLP concepts and feature engineering to improve recommendation accuracy by 22%; implemented responsible AI considerations including model interpretability and bias evaluation; followed software development best practices – version control (Git), unit testing, and technical documentation. Collaborated with cross-functional teams to understand requirements and deliver scalable AI solutions; participated in code reviews and basic troubleshooting of AI application components; continuously learned and applied emerging Generative AI concepts and tools.
View ProjectAWS Academy Cloud Foundations
Amazon Web Services
June 1, 2026 – Present
Python Essentials 1
Cisco
June 1, 2026 – Present
SQL and Relational Databases 101
IBM Cognitive Class
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects show a strong alignment with the target role of an AI Engineer, focusing on Generative AI and Machine Learning applications. The breadth of skills covers various aspects of AI development, from data pipelines to model deployment and responsible AI. The certifications in Python, SQL, and AWS indicate a proactive approach to skill development. However, the lack of diverse project types beyond core AI application development might suggest a narrower scope of experience, which could be a growth area for broader cultural fit in diverse engineering challenges.
Soft Skills & Operational Fit
The candidate demonstrates collaboration through participation in code reviews and working with cross-functional teams. There is an emphasis on continuous learning and applying emerging Generative AI tools and techniques. The projects indicate an understanding of best practices in model development, testing, documentation, and responsible AI considerations.