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AI/ML Engineer with 13 years of experience designing, building, and productionizing AI-enabled platforms, LLM applications, agentic workflows, and RAG systems across restaurant technology, eCommerce, insurance, financial workflows, hospitality, and enterprise SaaS. Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, FastAPI, Spark/Databricks, AWS/Azure/GCP, and production MLOps/LLMOps. Proven ability to move ambiguous AI ideas from prototype to reliable production using rigorous evaluation, guardrails, monitoring, CI/CD, and human-in-the-loop workflows.…AI/ML Engineer with 13 years of experience designing, building, and productionizing AI-enabled platforms, LLM applications, agentic workflows, and RAG systems across restaurant technology, eCommerce, insurance, financial workflows, hospitality, and enterprise SaaS. Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, FastAPI, Spark/Databricks, AWS/Azure/GCP, and production MLOps/LLMOps. Proven ability to move ambiguous AI ideas from prototype to reliable production using rigorous evaluation, guardrails, monitoring, CI/CD, and human-in-the-loop workflows.

Matthew Stevens

AI Engineer, Data Scientist, Full Stack Developer, +3

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AI/ML Engineer with 13 years of experience designing, building, and productionizing AI-enabled platforms, LLM applications, agentic workflows, and RAG systems across restaurant technology, eCommerce, insurance, financial workflows, hospitality, and enterprise SaaS. Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, FastAPI, Spark/Databricks, AWS/Azure/GCP, and production MLOps/LLMOps. Proven ability to move ambiguous AI ideas from prototype to reliable production using rigorous evaluation, guardrails, monitoring, CI/CD, and human-in-the-loop workflows.…AI/ML Engineer with 13 years of experience designing, building, and productionizing AI-enabled platforms, LLM applications, agentic workflows, and RAG systems across restaurant technology, eCommerce, insurance, financial workflows, hospitality, and enterprise SaaS. Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, FastAPI, Spark/Databricks, AWS/Azure/GCP, and production MLOps/LLMOps. Proven ability to move ambiguous AI ideas from prototype to reliable production using rigorous evaluation, guardrails, monitoring, CI/CD, and human-in-the-loop workflows.

Available to hire

AI/ML Engineer with 13 years of experience designing, building, and productionizing AI-enabled platforms, LLM applications, agentic workflows, and RAG systems across restaurant technology, eCommerce, insurance, financial workflows, hospitality, and enterprise SaaS.

Strong hands-on expertise in Python, SQL, PyTorch/TensorFlow, FastAPI, Spark/Databricks, AWS/Azure/GCP, and production MLOps/LLMOps. Proven ability to move ambiguous AI ideas from prototype to reliable production using rigorous evaluation, guardrails, monitoring, CI/CD, and human-in-the-loop workflows.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Lead Engineer / AI, ML Engineer at Culver Franchising System

March 1, 2020 - Present

Led architecture and delivery of AI-enabled restaurant, eCommerce, franchise operations, and internal automation platforms. Designed and shipped production LLM-powered assistants using FastAPI, AWS Bedrock/OpenAI/Anthropic Claude, LangChain/LlamaIndex, embeddings and vector search with RAG over operational knowledge bases. Built agentic workflows with tool calling, structured outputs, multi-step planning, retrieval, API actions, and human-in-the-loop review for support triage, order exception investigation, documentation lookup, inventory explanations, and franchise reporting. Implemented prompt versioning and evaluation loops tracking retrieval sources, confidence, hallucination risk, latency and token cost, and task-completion quality; developed reusable AI service layers for permission-aware retrieval, streaming responses, and backend-for-frontend integration. Built ML-ready feature pipelines (Python/SQL/Spark/PySpark/Airflow/AWS Glue/S3) and applied ML workflows for forecasting, a

Lead Engineer/ AI ,ML Engineer at Culver Franchising System

March 1, 2020 - Present

Led architecture and delivery of AI-enabled restaurant, eCommerce, franchise operations, and internal automation platforms. Designed and shipped production LLM-powered assistants using Bedrock/OpenAI/Claude, LangChain/LlamaIndex, embeddings, vector search, Elasticsearch/OpenSearch, and RAG pipelines for internal operational knowledge retrieval. Built agentic workflow patterns with tool calling, structured outputs, multi-step planning, retrieval, API actions, and human-in-the-loop review for support triage, order exception investigation, documentation lookup, inventory explanations, and reporting assistance. Established prompt engineering and evaluation loops tracking prompt versions, retrieval sources, confidence/hallucination risk, latency/cost, and task quality with feedback. Developed reusable AI service layers with FastAPI/REST/GraphQL, streaming, structured validation, permission-aware retrieval, and caching so product teams could integrate AI safely. Built ML-ready feature pipel

Senior AI/ML Engineer at Alstate Insurance Co

April 1, 2018 - December 1, 2019

Designed and delivered AI-enabled backend/data/automation systems for insurance and financial workflows including policy management, claims intake, billing operations, document processing, customer-service tools, reporting, reconciliation, and partner integrations. Built production data and ML-supporting services with Python/SQL, PostgreSQL/MongoDB/DynamoDB/Redis, FastAPI-style APIs, and event-driven processing (Lambda, SQS/SNS, RabbitMQ, CloudWatch). Implemented document intelligence workflows with preprocessing, metadata extraction, classification, retrieval indexing, and controlled outputs. Built RAG-style retrieval layers over policy/claims/billing/document knowledge using Elasticsearch/OpenSearch, embeddings-style indexing concepts, and metadata filters to improve search relevance and support lookup speed. Developed ML-ready pipelines with Glue/Airflow-style scheduling and ingestion/validation for structured/unstructured inputs. Supported applied ML use cases such as claims routi

AI-Focused Full Stack Developer at Comfort Inn North Bethesda

June 1, 2013 - October 31, 2017

Developed hospitality and property-management applications for reservations, guest profiles, check-in workflows, service requests, room availability, payment-adjacent processes, reporting, mapping, and guest communications. Built backend and data-processing features using Python, SQL, Flask/Django-style services, Node.js-style APIs, PostgreSQL/MySQL/MongoDB/Redis, and cloud environments including AWS and Azure. Created REST APIs and integration endpoints for customer-facing pages, internal property-management screens, reporting modules, and vendor systems. Implemented asynchronous processing for notifications, updates, alerts, reporting refreshes, escalations, and real-time staff communications. Improved application response times by 50% through SQL optimization, index tuning, Redis caching, schema cleanup, and refactoring. Integrated mapping workflows using property location data and third-party map APIs, and implemented secure, privacy-aware patterns for guest data and payment-adjac

AI-Focused Full Stack Developer at Comfort Inn

June 1, 2013 - October 1, 2017

Developed hospitality and property-management applications supporting reservations, guest profiles, check-in workflows, service requests, room availability, payment-adjacent processes, reporting, and operational dashboards across distributed hotel environments. Built backend and data-processing features using Python/SQL with Flask/Django-style services and Node.js-style APIs, integrating PostgreSQL/MySQL/MongoDB/Redis and cloud-hosted environments (AWS/Azure). Designed REST APIs and integration endpoints for customer-facing and internal property-management screens, vendor systems, mapping, and real-time guest workflows. Created asynchronous processing patterns for booking notifications, guest updates, operational alerts, and reporting refreshes. Improved response times by 50% using SQL optimization, indexing, Redis caching, schema cleanup, and refactoring. Implemented secure payment-adjacent and guest-data workflows with tokenized reference concepts, session security, RBAC, and audit