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pavan kalyan padala
Mid-level Data Scientist specializing in predictive and generative AI
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AI/ML engineer with production LLM experience in regulated financial services (J.P. Morgan Chase), building a customer response engine to automate first-contact resolution while addressing privacy, bias, compliance, and scale. Strong MLOps/orchestration background (Airflow, Docker/Kubernetes, AWS Step Functions, Azure ML/SageMaker) plus proven ability to integrate with legacy systems and drive stakeholder adoption through dashboards, auditability, and training.
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Key Strengths
- Led production deployment of an LLM-powered customer response engine for high-volume support
- Domain-specific LLM fine-tuning for financial queries with compliance considerations
- Built secure, scalable AI systems (privacy controls, encryption, audits, monitoring)
- Solved integration challenges with data silos and legacy systems via secure ETL and modular overlays
- Implemented explainability dashboards and automated audit logs to satisfy regulatory requirements
- Hands-on orchestration across Airflow, AWS Step Functions, Azure ML Pipelines; containerized deployments with Docker/Kubernetes
- Established rigorous evaluation and reliability practices (CV/holdout/simulations, KPI-driven monitoring, A/B tests, feedback loops)
- Drove adoption with training sessions/workshops for teams resistant to AI-driven processes
- Quantified automation impact: reduced manual effort by 30%+ on ETL/reporting workflows
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