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AI Engineer with 4+ years in Machine Learning, NLP, and Generative AI
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Data Scientist with 3.8 years of experience in transforming raw data into actionable insights. Skilled in machine learning, deep learning, and NLP with hands-on experience in building scalable AI solutions and data-driven applications.
SANVAN Software Limited
Data Scientist
March 1, 2024 – Present
India
Omega Healthcare Management Services Pvt Ltd
Data Scientist
October 1, 2023 – February 29, 2024
India
4D Global Medical Billing Services
Data Scientist
July 1, 2022 – September 30, 2023
India
Intelligent FAQ Bot for Healthcare (Spire Healthcare)
March 1, 2024 – Present
Designed and implemented a healthcare-specific RAG pipeline to answer patient queries based on internal documentation (policy PDFs, SOPs, treatment info). Pre-processed and vectorized 5000+ healthcare-related documents using sentence-transformers and stored embeddings in Pinecone for fast similarity search. Integrated LangChain agents with GPT-4 to create a contextual, multi-turn conversational experience, maintaining state and user context across interactions. Worked closely with SMEs and compliance officers to fine-tune NLP pipelines, ensuring sensitive information was masked and GDPR guidelines were followed. Implemented custom prompt templates for LLM-based responses to handle domain-specific queries. Developed evaluation pipelines using BLEU and ROUGE scores, and manually validated LLM responses using a confidence-based scoring system. Set up CI/CD workflows using Azure DevOps for model deployment and version control of LLM pipelines; retrained models quarterly with updated FAQs.
Insurance Claims Management & Revenue Cycle Support
October 1, 2023 – February 29, 2024
Managed end-to-end insurance claim follow-ups with US payers to ensure timely reimbursements and reduce pending AR. Analyzed and resolved claim denials by identifying root causes such as coding errors, eligibility issues, and documentation gaps. Processed and reviewed medical claims with 100% accuracy, ensuring compliance with payer rules and HIPAA standards. Collaborated with billing, coding, and client teams to clarify discrepancies and accelerate claim settlements. Monitored claim aging and prioritized high-value and time-sensitive claims to improve cash flow. Recognized as Best Employee of the Quarter for performance excellence, accuracy, and proactive issue resolution.
Fraud Detection in Banking (Judo Bank)
July 1, 2022 – September 30, 2023
Led design and implementation of a real-time fraud detection system monitoring high-frequency transactions from online and mobile banking applications. Engineered 70+ behavioral and transactional features (velocity features, time-of-day usage, device fingerprinting, geolocation anomalies) using PySpark pipelines. Built and evaluated multiple ML models (Random Forest, XGBoost, Isolation Forest) with SMOTE for class imbalance handling, achieving AUC > 0.97. Deployed models using Amazon SageMaker and integrated predictions into the fraud engine via Kafka streaming, enabling sub-second scoring. Implemented GenAI-based report generation for fraud investigation teams to automatically summarize flagged cases. Collaborated with risk and compliance teams to create interpretable explanations using SHAP values for every flagged transaction. Set up a feedback loop from fraud investigation outcomes, using confirmed labels to retrain and fine-tune models monthly.
Cultural Fit Analysis
The candidate's project diversity, ranging from healthcare FAQ bots to banking fraud detection, indicates adaptability and a broad interest in applying AI across different domains. Their experience in both traditional ML and advanced GenAI/LLM aligns well with the evolving landscape of AI engineering. The emphasis on compliance (GDPR, HIPAA) and interpretable AI (SHAP values) suggests a responsible and ethical approach to AI development, which is a strong cultural fit for many organizations.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through identifying root causes of claim denials and engineering features for fraud detection. Their collaboration with SMEs, compliance officers, and risk teams indicates good teamwork and communication. The focus on evaluation pipelines, feedback loops, and CI/CD suggests an operational mindset towards robust and maintainable AI systems.