Senior DevOps/ML Engineer with 18 years of systems and platform engineering experience, specializing in production-grade agentic AI and LLM platforms. Proven track record designing and operating stateful, multi-step AI w…
Senior DevOps/ML Engineer with 18 years of systems and platform engineering experience, specializing in production-grade agentic AI and LLM platforms. Proven track record designing and operating stateful, multi-step AI workflows, retrieval-augmented generation systems, and closed-loop ML decision engines deployed on Kubernetes.
Strong systems-first mindset applied to AI: agent orchestration, failure handling, observability, cost controls, safe rollouts, and compliance-aware design. Experienced in building LLMOps foundations including model routing, monitoring, drift detection, and rollback strategies for real-world enterprise environments.
Particularly effective in roles that sit at the intersection of distributed systems, ML platforms, and customer-facing AI infrastructure, where reliability, explainability, and operational safety matter as much as model quality.
Designed and operated cloud-native platforms supporting AI and ML workloads in regulated healthcare environments
Enabled production ML pipelines using Kubernetes-based CI/CD, monitoring, and safe deployment patterns
Partnered with data science teams to productionize ML use cases with reliability and observability baked in
Introduced predictive scaling and automated recovery mechanisms for AI services
Ensured compliance, stability, and operational readiness for ML systems used in critical environments
Designed and operated cloud-native platforms supporting AI and ML workloads in regulated healthcare environments. Enabled production ML pipelines using Kubernetes-based CI/CD, monitoring, and safe deployment patterns. Partnered with data science teams to productionize ML use cases with reliability and observability baked in. Introduced predictive scaling and automated recovery mechanisms for AI services. Ensured compliance, stability, and operational readiness for ML systems used in critical environments.
Owned and operated large-scale infrastructure supporting 300+ engineers
Built predictive monitoring and anomaly detection systems using Python-based classifiers
Improved capacity planning accuracy and reduced storage costs by ~20%
Worked closely with cross-functional teams to improve CI reliability and system stability
Owned and operated large-scale infrastructure supporting 300+ engineers. Built predictive monitoring and anomaly detection systems using Python-based classifiers. Improved capacity planning accuracy and reduced storage costs by ~20%. Worked closely with cross-functional teams to improve CI reliability and system stability.
Built and stabilized large-scale build, test, and deployment systems
Introduced automation and analytics to improve reliability and throughput
Early adopter of data-driven approaches to quality and systems engineering
Designed and deployed ML models to predict and classify operational incidents from telemetry-like signals (metrics, errors, deployment events)Addressed extreme class imbalance (~3% base rate) using feature engineering, threshold tuning, and recall-optimized evaluationIntegrated predictions into automated response and alerting workflows, enabling early interventionDeployed as a containerized, monitored API with rollback supportImpact: Reduced reactive firefighting by enabling early warning signals and data-driven operational decisions.
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