Akshay Khan

About

Backend engineer with American Express experience maintaining an internal Python/Flask rewards simulation microservice used by product analysts and QA. Demonstrated strong performance and scalability work: moved batch simulations to Celery, added Redis caching to cut DynamoDB latency, and tuned Postgres/SQLAlchemy queries with EXPLAIN ANALYZE and composite indexes (bringing API responses under ~200ms by queueing jobs). Also has experience integrating ML via Flask-based model-serving APIs (scikit-learn/LightGBM packaged with joblib) and designing multi-tenant data isolation and tenant-specific configuration systems.

Key Strengths

• Designed maintainable Flask microservice architecture (blueprints + service/access layers)
• Performance optimization by offloading heavy request work to Celery workers
• Reduced DynamoDB latency via Redis caching of rule metadata
• SQL performance tuning using SQLAlchemy logging and Postgres EXPLAIN ANALYZE
• Improved slow joins with composite indexing and selective column loading
• Multi-tenant isolation with tenant-scoped configs and cache key namespacing
• High-throughput API optimization (async job queueing reduced responses to <200ms)
• Takes LLM prototypes to production by aligning business goals with reliability/latency/cost/data-quality requirements
• Designs resilient systems with timeouts, retries, and graceful fallback behavior
• Mitigates retry storms under traffic spikes using stricter timeouts, concurrency limits, and caching
• Systematic real-time debugging across prompt, retrieval, and downstream services using logs/traces and controlled inputs
• Reduces regression risk via automated tests, feature-flagged deployments, controlled rollouts, and metric monitoring
• Strong technical communication: structures demos/workshops from outcomes to end-to-end flow, then architecture/tradeoffs
• Effective cross-functional partner to sales: runs discovery, clarifies usage patterns, and builds focused POCs to address scale concerns

Experience

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