Abhijeet Anand
AI Research EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Research Engineer with 1+ years in Machine Learning & Deep Learning
Highly motivated Computer Science and Engineering professional pursuing a Master's from IIT Kharagpur, specializing in Machine Learning, Deep Learning, and NLP. Achieved top ranks in national competitive exams (GATE CSE, IIIT Hyderabad PGEE). Proven ability in developing end-to-end ML pipelines for cancer prediction and fake news detection, and building multi-agent AI systems for literature review generation. Proficient in Python, TensorFlow, PyTorch, and various ML techniques, with a strong academic background and leadership experience as a Teaching Assistant and Mentor.
Indian Institute of Technology, Kharagpur
Master of Technology · Computer Science and Engineering
August 1, 2024 – June 30, 2026
IPS Academy
Bachelor of Technology · Computer Science and Engineering
August 1, 2020 – June 30, 2024
IIT Kharagpur
Teaching Assistant
July 1, 2025 – December 1, 2025
Kharagpur, West Bengal, India
Gate@Zeal Coaching Institute
Mentor
March 1, 2023 – May 1, 2024
India
ARIA: Multi-Agent Research Intelligence System
February 1, 2026 – Present
Developed a 4-agent LangGraph-based AI agent system that generates structured literature reviews from queries. Implemented retrieval-augmented generation (RAG) using ChromaDB and embeddings for semantic evidence retrieval. Built automated workflows for research paper retrieval, ranking, deduplication, and structured knowledge extraction. Improved system reliability through persistent storage, fault-tolerant execution, and comprehensive automated testing.
Cancer Prediction Pipeline using Machine Learning and Deep Learning
July 1, 2025 – Present
Built an end-to-end ML pipeline using scikit-learn with scaling, PCA/SelectKBest, SMOTE, and GridSearchCV. Optimized RBF-SVM using hyperparameter tuning and K-Fold CV, improving accuracy to 92% with AUC of 0.9167. Architected a CNN-SVM hybrid pipeline using TensorFlow for deep feature extraction from medical images. Validated the hybrid model across K = 2-20 folds, consistently achieving 88-93% accuracy with AUC above 0.93.
Seq2Seq and Transformer Models for Text Summarization & Title Generation
March 1, 2025 – Present
Built Seq2Seq RNN with Bi-GRU for Wikipedia title generation on 14k articles, achieving ROUGE-1 of 0.7244. Improved RNN using GloVe embeddings, hierarchical encoding, and beam search, enhancing title generation coherence. Fine-tuned T5-small with Hugging Face Transformers and hyperparameter tuning to improve summarization quality. Applied zero-shot Flan-T5-large with custom prompts, achieving ROUGE-1 of 0.863 and ROUGE-2 of 0.752.
COVID-19 Fake News Detection using Machine Learning
February 1, 2025 – Present
Built an end-to-end COVID-19 fake-news classifier on 10,600 social posts with TF-IDF and robust social-text cleaning. Preprocessed social media text, demojizing emojis, handling URLs/hashtags to preserve context for accurate detection. Trained and optimized Logistic Regression, SVM, KNN, and Gradient Boosting models using GridSearchCV. Delivered best results with SVM (94% accuracy) and Logistic Regression (F1-score: 0.94), outperforming baselines.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and capability in cutting-edge AI research, aligning well with an AI Research Engineer role. The diversity of projects (text summarization, fake news detection, cancer prediction, multi-agent systems) shows a broad intellectual curiosity and adaptability. The high academic achievements and competitive exam results suggest a driven and high-achieving individual. However, all projects are academic, and there is no information on collaborative work within a professional team setting, which is crucial for cultural fit in an industry role.
Soft Skills & Operational Fit
The candidate's experience as a Teaching Assistant and Mentor indicates strong communication and pedagogical skills, which are valuable for explaining complex technical concepts and collaborating within a team. The project descriptions suggest an ability to work on complex, multi-faceted problems and deliver measurable results. However, there is no direct evidence of operational experience (e.g., MLOps, deployment, production systems) or specific soft skills like leadership or conflict resolution beyond mentoring.