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Nikita Prasad
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable data pipelines
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Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.
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Key Strengths
- Built and deployed an LLM-powered real-time financial content recommendation/engagement assistant to production
- Privacy-by-design implementation for sensitive financial data (masking/tokenization/encryption, least-privilege access, auditable pipelines)
- Efficient model adaptation using LoRA without full retraining
- Production-grade MLOps: containerized microservices, continuous monitoring, and drift detection
- Orchestrated end-to-end ML workflows with Apache Airflow (ingestion through retraining) with robust failure/retry handling
- Integrated Airflow with MLflow for experiment tracking, artifact/metric logging, and auditability
- Structured evaluation approach: modular agent/workflow design, unit/integration tests, offline metrics + A/B testing and feedback loops in production
- Strong cross-functional collaboration with product, risk, and compliance stakeholders; translates regulatory constraints into technical requirements
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“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
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