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AI/ML engineer with 10+ years building and operating production ML pipeline and API infrastructure for product teams. Focused on end-to-end model lifecycle: training with PyTorch/TensorFlow, feature pipelines, and production deployment on Kubernetes and AWS, supporting high scale and reliability for analytics and product use cases. Led production ML CI/CD, monitoring and automated retraining, and improved inference latency and cost using ONNX, batching, and performance optimizations. Owns ML Ops practices including data validation, lineage tracking, reproducible experiments, drift monitoring, and cost/capacity planning, while partnering with product and backend teams to deliver well-defined API endpoints and SLAs.…AI/ML engineer with 10+ years building and operating production ML pipeline and API infrastructure for product teams. Focused on end-to-end model lifecycle: training with PyTorch/TensorFlow, feature pipelines, and production deployment on Kubernetes and AWS, supporting high scale and reliability for analytics and product use cases. Led production ML CI/CD, monitoring and automated retraining, and improved inference latency and cost using ONNX, batching, and performance optimizations. Owns ML Ops practices including data validation, lineage tracking, reproducible experiments, drift monitoring, and cost/capacity planning, while partnering with product and backend teams to deliver well-defined API endpoints and SLAs.

James Zamora

AI Engineer, Data Engineer, DevOps Developer, +2

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AI/ML engineer with 10+ years building and operating production ML pipeline and API infrastructure for product teams. Focused on end-to-end model lifecycle: training with PyTorch/TensorFlow, feature pipelines, and production deployment on Kubernetes and AWS, supporting high scale and reliability for analytics and product use cases. Led production ML CI/CD, monitoring and automated retraining, and improved inference latency and cost using ONNX, batching, and performance optimizations. Owns ML Ops practices including data validation, lineage tracking, reproducible experiments, drift monitoring, and cost/capacity planning, while partnering with product and backend teams to deliver well-defined API endpoints and SLAs.…AI/ML engineer with 10+ years building and operating production ML pipeline and API infrastructure for product teams. Focused on end-to-end model lifecycle: training with PyTorch/TensorFlow, feature pipelines, and production deployment on Kubernetes and AWS, supporting high scale and reliability for analytics and product use cases. Led production ML CI/CD, monitoring and automated retraining, and improved inference latency and cost using ONNX, batching, and performance optimizations. Owns ML Ops practices including data validation, lineage tracking, reproducible experiments, drift monitoring, and cost/capacity planning, while partnering with product and backend teams to deliver well-defined API endpoints and SLAs.

Available to hire

AI/ML engineer with 10+ years building and operating production ML pipeline and API infrastructure for product teams. Focused on end-to-end model lifecycle: training with PyTorch/TensorFlow, feature pipelines, and production deployment on Kubernetes and AWS, supporting high scale and reliability for analytics and product use cases.

Led production ML CI/CD, monitoring and automated retraining, and improved inference latency and cost using ONNX, batching, and performance optimizations. Owns ML Ops practices including data validation, lineage tracking, reproducible experiments, drift monitoring, and cost/capacity planning, while partnering with product and backend teams to deliver well-defined API endpoints and SLAs.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

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Expert

Expert

Expert

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Expert

Intermediate

Work Experience

Senior Data Engineer / AI/ML Engineer at Uplabs

January 1, 2023 - July 1, 2026

Led delivery of production ML model pipelines that trained weekly and served 1.2M monthly users, orchestrated with Airflow and deployed to Kubernetes on AWS. Built feature and data pipelines in Python and SQL processing 3–8 TB/day from streaming and batch sources, reducing downstream query times by 45% via columnar storage and partitioning. Owned ML Ops lifecycle: automated data validation, lineage tracking, reproducible ML experiments, and retraining schedules to limit drift. Implemented model CI/CD using Docker, GitHub Actions, and ArgoCD (training → validation → deployment) to cut manual release time from days to under 4 hours. Built model serving stack with FastAPI and gRPC, containerized with Docker and auto-scaled on Kubernetes; sustained 2,000+ RPS peak with 120ms median latency. Established monitoring and alerting with Prometheus and Grafana, instrumented inference-quality metrics, and reduced unnoticed drift incidents by 60% year-over-year. Optimized inference using ON

Senior Data Engineer at Uplabs

January 1, 2023 - July 1, 2026

Led delivery of production model pipelines trained weekly and serving 1.2M monthly users. Orchestrated with Air and deployed on Kubernetes on AWS. Built feature pipelines in Python and SQL processing 3–8 TB/day from streaming and batch sources, reducing downstream query times by ~45% via columnar storage and partitioning. Implemented ML CI/CD using Docker, GitHub Actions, and ArgoCD to automate training → validation → deployment, cutting manual release time from days to under 4 hours. Built model serving stack with FastAPI and gRPC, containerized with Docker, autoscaled on Kubernetes, sustaining 2,000+ RPS with 120 ms median latency. Owned MLOps lifecycle with automated data validation, lineage tracking, reproducible ML experiments, and retraining schedules to limit drift. Established monitoring/alerting with Prometheus/Grafana and reduced unnoticed drift incidents by 60% year-over-year. Optimized inference performance by converting models to ONNX and batching requests, lowering

Software Engineer at Tech Websters

January 1, 2018 - December 1, 2022

Built end-to-end data pipelines ingesting 1–4 TB/day using Apache Spark and batch jobs to the analytics warehouse and early ML experiments. Developed and trained TensorFlow and scikit-learn models, shipped features that increased product engagement metrics, and packaged models into Docker containers deployed as prototype inference endpoints. Authored SQL-heavy feature extraction layers and performance-tuned queries on PostgreSQL and Redshift to support nightly model training jobs. Designed APIs for model inference using Flask, documented API contracts, and worked with frontend and mobile teams to integrate predictions into user experiences. Orchestrated ETL and ML training workflows with Airflow, introducing retry policies and SLA checks to improve pipeline reliability. Implemented streaming ingestion with Kafka to capture product events and built micro-batches feeding feature pipelines in Spark to improve data freshness to <15 minutes. Led migration of nightly Hadoop jobs to Spark

Software Engineer, Tech/Web at Tech Websters

January 1, 2018 - December 1, 2022

Built end-to-end data pipelines ingesting 1–4 TB/day using Apache Spark and batch jobs, feeding an analytics data warehouse and early ML experiments. Developed and trained TensorFlow and scikit-learn models and shipped features increasing product engagement metrics. Packaged models into Docker containers and deployed prototype serving endpoints; collaborated with ops to move workloads to Kubernetes clusters for staging. Authored SQL-heavy feature extraction layers and performance-tuned queries on PostgreSQL and Redshift to support nightly model training jobs. Designed APIs for model inference using Flask, including documentation of API contracts and worked with frontend/mobile teams to integrate predictions. Built orchestration with Air to schedule ETL/ML training DAGs, adding retry policies and SLA checks to improve reliability. Introduced experiment tracking and standardized model packaging/reproducibility across multiple data scientists and engineers. Implemented streaming ingesti

Data Engineer at FineLabs

May 1, 2015 - July 1, 2017

Built core ETL pipelines in Python and Scala consolidating logs and transactional data into a reporting data warehouse, processing ~500 GB/night. Implemented batch processing with Hadoop/Spark, converting legacy MapReduce jobs to improve throughput and maintainability. Modeled data warehouses with dimensional schemas on PostgreSQL and delivered nightly aggregates powering BI dashboards. Authored SQL reports and tuned queries; created indexed materialized views to speed up recurring analytics queries by 4x. Automated deployments/releases for data jobs using Jenkins and shell scripting, reducing deployment errors and manual steps. Served on-call for ETL jobs—triaged and mediated pipeline failures and developed runbooks cutting incident resolution time in half. Built an internal data-validation framework comparing nightly outputs to baseline expectations to surface schema and volume regressions. Led a data ops program centralizing logging and error reporting for data jobs to enable proa