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Srikanth Reddy
Mid-level AI/ML Engineer specializing in GenAI and financial risk & compliance analytics
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About
Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.
Experience
Education
Key Strengths
- Built and deployed an LLM-powered financial risk & compliance platform in production
- Designed multi-agent LLM workflows (LangChain) for trade exception classification and regulatory/risk document summarization
- Integrated structured (Redshift) and unstructured (vector DB) data via Python/SQL ETL pipelines
- Focused on reducing LLM hallucinations for high-stakes compliance use cases (used Amazon Bedrock)
- Hands-on orchestration for ML/ETL workflows using Kubernetes, Airflow, and Jenkins
- Established measurable evaluation and reliability practices (KPIs, cross-validation, A/B testing, drift monitoring with Evidently AI/MLflow, automated tests with PyTest)
- Effective collaboration with non-technical compliance officers/risk managers using prototypes, dashboards, and iterative feedback
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