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Machine Learning Engineer with 1+ years in LLM applications & RAG pipelines
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AI-focused Software Engineer skilled in LLM applications, RAG pipelines, and machine learning using Python. Experienced in building scalable AI solutions with LangChain, vector databases, and NLP techniques. Proficient in backend development, API integration, and problem-solving, with strong collaboration skills and the ability to deliver efficient, production-ready applications in Agile environments.
Gyan Ganga Institute of Technology and Sciences
Bachelor of Technology · Electronics and Communication
August 1, 2020 – June 30, 2024
Tata Consultancy Services
Machine Learning Engineer
October 1, 2024 – Present
Pune, Maharashtra, India
Book Recommendation System
January 1, 2024 – October 1, 2024
Built a ML-based book recommendation system using collaborative and content-based filtering on 10,000+ interactions. Enhanced recommendations using feature engineering, increasing relevance by 25% and engagement by 30%. Optimized model performance, reducing latency by 20% and improving system scalability. Tech Stack: Python, Machine Learning, Scikit Learn, Streamlit
View ProjectCultural Fit Analysis
The candidate's experience with diverse AI projects (recommendation systems, LLM agents) and collaboration in Agile teams suggests adaptability. The role at Tata Consultancy Services aligns well with a professional, structured environment. The breadth of skills listed (C++, Python, SQL, various ML libraries, Git) indicates a willingness to learn and apply different technologies, which is positive for cultural fit in a dynamic tech company. However, with only one professional experience and one personal project, the diversity of environments and team interactions is somewhat limited for a senior role.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration in Agile environments and problem-solving skills. The project descriptions are clear and concise, suggesting good communication. The focus on optimizing performance and scalability indicates an operational mindset. However, without specific psychometric or English test results, a deeper assessment of soft skills and operational fit is limited.