Senior AI Platform Engineer at Tecton (2023-08 – 2026-01)
ML Feature Platform for Real-Time Machine Learning Systems
- Architected real-time feature infrastructure powering ML models used in recommendation and fraud detection systems, enabling feature retrieval with sub-100ms latency across millions of predictions per day.
- Engineered distributed feature ingestion pipelines using Python, Go, and Kafka that processed 50M+ feature updates daily across streaming and batch ML pipelines.
- Developed retrieval-augmented generation workflows integrating vector search pipelines and LLM-driven data retrieval, enabling ML teams to build AI-assisted analytics and knowledge systems.
- Orchestrated scalable microservices on AWS (EKS, DynamoDB, S3, Lambda) allowing horizontal scaling of feature pipelines and increasing ML infrastructure throughput by 2×.
- Redesigned feature materialization pipelines, lowering compute overhead by 35% while improving freshness of training and inference features across multiple ML models.
- Established observability pipelines using OpenTelemetry, Prometheus, and allocated tracing that reduced production incident detection time by 40%.
- Accelerated ML model deployment pipelines by integrating automated feature validation and CI/CD workflows, reducing model rollout cycles by 30%.
Backend / AI Systems Engineer at Gatekeeper (2021-06 – 2023-07)
Vendor & Contract Lifecycle Management SaaS Platform
- Constructed scalable backend services supporting Gatekeeper's enterprise procurement platform used by 1000+ organizations managing millions of contracts and vendor records.
- Designed allocated API infrastructure using Python and Go enabling secure multi-tenant processing of procurement and compliance data across large enterprise datasets.
- Integrated NLP-assisted document analysis pipelines for contract classification and metadata extraction, improving document processing automation accuracy by 35%.
- Optimized database access strategies through query tuning and indexing improvements, decreasing API response latency by 45% across critical contract management endpoints.
- Deployed asynchronous workflow pipelines with Redis queues to support large-scale contract automation jobs, boosting background task throughput by 60%.
- Implemented third-party integration layers connecting CRM, procurement platforms, and identity providers via secure REST APIs and event pipelines.
- Strengthened deployment reliability by introducing automated testing frameworks and CI/CD pipelines, lowering release failure rates by 30%.
Distributed Systems Engineer at Dreamix (2019-11 – 2021-04)
Enterprise Analytics & Cloud Data Platforms
- Delivered backend services for enterprise analytics platforms supporting real-time business intelligence systems processing 10M+ records per day.
- Built provisioned data processing microservices using Python and Go enabling scalable ingestion pipelines and increasing data throughput by 2.5×.
- Containerized analytics services with Docker and orchestrated workloads using Kubernetes, maintaining 99.9% service availability under high traffic conditions.
- Introduced provisioned caching layers using Redis and PostgreSQL query optimization techniques that reduced analytics query latency by 40%.
- Constructed API layers for enterprise dashboards, enabling product teams to deliver near-real-time reporting insights across multi-tenant datasets.
- Established monitoring and observability frameworks with Prometheus and ELK stack improving system diagnostics and decreasing deployment failures by 30%.
Full-Stack Developer at Arcanys (2018-09 – 2019-09)
Cloud-Based SaaS Platforms for Global Clients
- Developed SaaS web platforms for international clients delivering scalable backend APIs and responsive web interfaces across cloud environments.
- Implemented backend services using Python and Node.js supporting REST APIs consumed by web and mobile applications across multiple production deployments.
- Built interactive frontend dashboards with React, improving user interaction performance and reducing page load times by 25%.
- Structured modular service architecture that simplified feature expansion and decreased development cycle time for new product releases.
- Collaborated in Agile development teams, delivering production features across rapid sprint cycles while supporting international engineering teams.
