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AI Engineer with less than a year in LLM pipelines, RAG, and NLP for real-world systems.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
GenAI Engineer skilled in designing and implementing multilingual LLM pipelines, RAG workflows, LLM-based token classification, and natural language understanding for real-world systems. Experienced in creating AI workflows using leading models such as Llama-3, GPT models, and mBERT/XLM-R with strong fundamentals in algorithms, distributed systems and scalable backend development.
Institute of Engineering and Technology, Lucknow
B. Tech. · Computer Science and Engineering
August 1, 2021 – June 30, 2025
JSPT Pvt. Ltd.
Technical Fellow
May 1, 2025 – October 1, 2025
Patna, Bihar, India
Little Bear Labs
Software Developer Intern
July 1, 2024 – September 1, 2024
USA
Hindi-to-ISL Multilingual LLM Translator
November 1, 2024 – April 1, 2025
Designed a multilingual LLM-based Hindi → ISL translation pipeline using instruction-tuned LLMs (Llama-3 GPT models) with zero-shot POS tagging, NER and gloss generation. Used LLM embeddings to map Hindi tokens to ISL glosses with fallback to fingerspelling for OOV vocabulary. Achieved 91% translation accuracy (benchmarked on custom Hindi-ISL dataset with BLEU and human evaluation).
View ProjectCustomer Churn Prediction System
August 1, 2023 – October 1, 2023
Built an end-to-end ML pipeline to predict customer churn using transactional data. Performed exploratory data analysis, feature engineering, and handled class imbalance to improve model robustness. Trained and compared multiple models including Logistic Regression, Random Forest, and XGBoost, achieving 89% accuracy and 84% recall on a 50K-record churn dataset.
Cultural Fit Analysis
The candidate's project diversity, ranging from multilingual LLM translation to customer churn prediction and LLM workflow automation, indicates adaptability and a broad interest in AI applications. Their involvement in competitive programming (Codeforces, LeetCode) suggests a strong drive for continuous improvement and a competitive spirit, which can be a good cultural fit for fast-paced, innovation-driven environments. The target role of 'AI Engineer' aligns well with their demonstrated skills and project experience, particularly in GenAI and NLP. However, the experience level is '0', indicating a recent graduate, which might require more mentorship in a professional setting.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving abilities and a proactive approach to learning and applying advanced AI/ML techniques. Their experience with remote execution pipelines suggests an understanding of operational challenges in AI development. The academic projects and competitive programming achievements indicate a driven and analytical mindset. However, without specific psychometric or English test results, a comprehensive assessment of soft skills like communication clarity, work attitude, stress handling, and team collaboration is limited.