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Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
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About
Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.
Experience
Education
Key Strengths
- Built and operated a stateless async FastAPI service for real-time ML/LLM inference on Kubernetes
- Strong reliability engineering: explicit timeouts at boundaries, idempotent retries with backoff/jitter, Redis deduplication
- Production AWS delivery with EKS + Lambda, GitHub Actions CI/CD, Helm releases and automated rollbacks
- Secrets and access control using AWS Secrets Manager and IAM roles (no hard-coded secrets)
- Data engineering with AWS Glue/PySpark integrating S3, Kafka, and Snowflake; schema evolution and data quality checks with CloudWatch metrics
- Modernized legacy batch/Excel/SQL logic into Python microservices with parallel-run parity validation and phased cutover using feature flags
- SQL performance optimization in PostgreSQL: reduced query latency from 20–30s to <3s and cut DB CPU ~50–60% via indexing, rewrites, and partitioning
- Owned pipeline incident response: CloudWatch detection, log/metric triage, checkpointed reruns, DLQ for malformed records, improved monitoring and change management
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