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Kalyaan Kanugula
Mid-Level Software Engineer specializing in AWS distributed systems and microservices
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About
Backend/ML-systems engineer with experience (including Amazon) building real-time face recognition services using PyTorch (MTCNN/FaceNet) and AWS (SQS/S3/Lambda/EC2) with a focus on low latency, burst handling, and cost control. Also led a revenue-critical legacy pricing workflow migration to a serverless event-driven architecture using strangler-pattern rollout, simulation-based validation, and strong security practices (JWT/RBAC/RLS).
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Key Strengths
- Designed low-latency, fault-tolerant ML inference pipeline handling ~1,000 concurrent requests
- Decoupled compute-heavy inference via SQS-based event-driven architecture to absorb burst traffic
- Implemented idempotency and duplicate-processing protection for at-least-once delivery semantics
- Cost-aware autoscaling strategy (queue-depth scaling, scale-down delay, warm-worker baseline) balancing P95 latency vs spend
- Led revenue-critical migration using strangler pattern with parallel runs, traffic mirroring, and measurable validation gates
- Strong API engineering practices in FastAPI (strict schemas, versioning, consistent errors, stateless async scaling)
- Production security implementation (OAuth2/JWT, RBAC, secrets management, database-enforced RLS)
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