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I'm Daryl Alford, a Senior AI Platform Engineer specializing in building and operating production-grade ML and LLM systems across fintech and enterprise platforms. I focus on Retrieval-Augmented Generation (RAG), fraud-detection modeling, distributed PyTorch training, and scalable AI infrastructure on AWS and Kubernetes. I design end-to-end AI systems that drive measurable improvements in precision, latency, and cost efficiency. I collaborate with cross-functional teams to implement robust AI solutions—from feature engineering and model training to distributed inference, monitoring, retraining automation, and governance—keeping systems secure, auditable, and compliant while delivering tangible business impact.…I'm Daryl Alford, a Senior AI Platform Engineer specializing in building and operating production-grade ML and LLM systems across fintech and enterprise platforms. I focus on Retrieval-Augmented Generation (RAG), fraud-detection modeling, distributed PyTorch training, and scalable AI infrastructure on AWS and Kubernetes. I design end-to-end AI systems that drive measurable improvements in precision, latency, and cost efficiency. I collaborate with cross-functional teams to implement robust AI solutions—from feature engineering and model training to distributed inference, monitoring, retraining automation, and governance—keeping systems secure, auditable, and compliant while delivering tangible business impact.

Daryl Alford

AI Engineer, Data Scientist, Full Stack Developer, +2

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I'm Daryl Alford, a Senior AI Platform Engineer specializing in building and operating production-grade ML and LLM systems across fintech and enterprise platforms. I focus on Retrieval-Augmented Generation (RAG), fraud-detection modeling, distributed PyTorch training, and scalable AI infrastructure on AWS and Kubernetes. I design end-to-end AI systems that drive measurable improvements in precision, latency, and cost efficiency. I collaborate with cross-functional teams to implement robust AI solutions—from feature engineering and model training to distributed inference, monitoring, retraining automation, and governance—keeping systems secure, auditable, and compliant while delivering tangible business impact.…I'm Daryl Alford, a Senior AI Platform Engineer specializing in building and operating production-grade ML and LLM systems across fintech and enterprise platforms. I focus on Retrieval-Augmented Generation (RAG), fraud-detection modeling, distributed PyTorch training, and scalable AI infrastructure on AWS and Kubernetes. I design end-to-end AI systems that drive measurable improvements in precision, latency, and cost efficiency. I collaborate with cross-functional teams to implement robust AI solutions—from feature engineering and model training to distributed inference, monitoring, retraining automation, and governance—keeping systems secure, auditable, and compliant while delivering tangible business impact.

Available to hire

I’m Daryl Alford, a Senior AI Platform Engineer specializing in building and operating production-grade ML and LLM systems across fintech and enterprise platforms. I focus on Retrieval-Augmented Generation (RAG), fraud-detection modeling, distributed PyTorch training, and scalable AI infrastructure on AWS and Kubernetes. I design end-to-end AI systems that drive measurable improvements in precision, latency, and cost efficiency.

I collaborate with cross-functional teams to implement robust AI solutions—from feature engineering and model training to distributed inference, monitoring, retraining automation, and governance—keeping systems secure, auditable, and compliant while delivering tangible business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Senior AI Platform Engineer at Fearless Solutions

July 1, 2024 - September 1, 2025

Designed and operated a production-grade AI platform supporting LLM-driven retrieval and analyst workflows. Architected secure RAG pipelines using LangChain, FAISS, and Pinecone, reducing retrieval latency by 50%. Built streaming and batch embedding pipelines with Kafka and Airflow for document ingestion and indexing. Implemented prompt versioning and experiment tracking via MLflow and Weights & Biases. Fine-tuned transformer models with LoRA and distributed PyTorch (DDP/FSDP), lowering GPU training costs by 25%. Deployed scalable FastAPI-based inference services on Kubernetes with autoscaling and integrated OpenTelemetry for latency monitoring and SLA tracking. Implemented retrieval confidence scoring and citation grounding to mitigate hallucinations. Improved analyst productivity by 45% with AI-assisted retrieval systems. Built evaluation pipelines for RAG systems and implemented regression testing and drift detection for embeddings and prompts. Integrated CI/CD testing for inference

Senior Machine Learning Engineer at Fast

January 1, 2021 - April 1, 2024

Designed and deployed fraud detection models processing real-time financial transaction streams. Engineered feature pipelines for high-cardinality merchant data and time-window transaction features. Addressed class imbalance with resampling, threshold tuning, and cost-sensitive learning. Improved fraud detection precision by 28% and reduced false positives by 35%. Built low-latency inference APIs for real-time risk scoring. Optimized SQL-based data extraction pipelines for large-scale training datasets. Implemented model monitoring dashboards and automated retraining pipelines triggered by drift detection. Deployed containerized ML services to AWS EKS with autoscaling and integrated MLflow model registry. Collaborated with compliance and risk teams ensuring auditability and transparency.

AI & Backend Engineer at Social Solutions

October 1, 2019 - December 1, 2020

Developed ML-powered analytics systems supporting anomaly detection and operational forecasting. Built supervised models using Scikit-learn and XGBoost; performed feature engineering and data preprocessing on enterprise datasets. Designed training workflows for periodic batch retraining; implemented unit tests for feature components and created evaluation frameworks tracking precision, recall, and F1. Built dataset validation checks and integrated secure REST APIs for inference in FastAPI. Modeled PostgreSQL schemas for analytics workloads and delivered predictive services into enterprise platforms.

Software Engineer (Intern) at Social Solutions

July 1, 2017 - September 1, 2019

Developed backend systems and data-processing modules; assisted in ML experimentation workflows and feature extraction tasks; contributed to system debugging and performance optimization; participated in Agile sprint cycles delivering iterative AI enhancements.