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Sharath Pampalker
Mid-level AI/ML Engineer specializing in real-time anomaly detection and AI agents
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Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.
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Key Strengths
- Built and deployed real-time anomaly detection for HFT in production
- Improved anomaly detection recall by ~25%
- Designs low-latency ML systems balancing accuracy with compliance/SLA constraints
- Production MLOps: Docker/Kubernetes/SageMaker with MLflow + Grafana monitoring
- Safe production rollout practices (canary releases, rolling updates, A/B testing)
- Designs reliable AI agents/workflows with multi-level testing and guardrails
- Strong cross-functional communication with risk/compliance; translates ML trade-offs into business terms
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