Led the design of a policy-governed autonomous remediation platform to enable safe automation of production incident workflows.

Introduced a layered architecture where AI-generated recommendations are constrained by deterministic policy controls, ensuring reliable execution across environments.

Key contributions include:

• Designed governance model with autonomy levels, blast-radius controls, and escalation contracts (approval, investigation, SME review)

• Built a reusable decision framework standardizing incident response logic across services

• Implemented structured decision logging and lifecycle traceability for audit, replay, and observability

• Introduced staged rollout strategies (shadow → controlled → live) to safely adopt autonomous systems

Focus areas: AI systems, SRE, distributed systems, AWS, system reliability

Worked on large-scale distributed data processing systems using Spark and Kafka, enabling reliable ingestion and processing of high-volume financial datasets.

Designed and optimized data pipelines with a focus on fault tolerance, scalability, and operational reliability.

Contributed to improving consistency and maintainability by introducing reusable patterns for pipeline design, monitoring, and failure handling.

Integration Specialist - webMethods