Mid-Level AI Engineer Resume

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Mid-Level AI Engineer Resume Jaylen Callahan Mid-Level AI Engineer (513) 555-0184 jaylen.callahan.ai@example.com linkedin.com/example/jaylencallahan Cincinnati, OH 45202 STRENGTHS Operational product thinking Turned planner interviews into a usable AI backlog; that early clarity helped engineers focus on decisions that mattered in production. Practical AI communication Explained confidence intervals, tradeoffs, and model limits to executives and domain experts; leaders gained a clearer basis for action. Reliable delivery habits Added testing, registry controls, and release checks around model APIs; teammates began seeking advice on safer production launches. Collaborative mentoring Coached four engineers through prompt reviews, explainability, and secure coding; shared practice became part of everyday delivery. Responsible model judgment Used SHAP, LIME, error analysis, and drift alerts to question model behavior early, giving users more confidence in recommendations. SKILLS Python Java TypeScript SQL FastAPI Flask REST APIs AWS Databricks MLflow Prompt engineering RAG Vector databases Anomaly detection A/B testing LANGUAGES English Native 40 Spanish Intermediate 20 MY CAREER 4 Years AI Engineer at Northstar Manufacturing Analytics (1.6 Years) Machine Learning Engineer at BlueRidge Industrial Software (1.6 Years) Associate Data Scientist at Riverbend Operations Lab (10 Months) SUMMARY Mid-level AI Engineer with 4+ years delivering production machine learning systems and AI-powered applications for manufacturing, supply chain, and operations analytics. Builds Python services, forecasting and anomaly detection models, LLM applications , MCP servers , and REST APIs across AWS and Databricks environments. Owns work from requirements discovery and feature engineering through training, evaluation, deployment, monitoring, and iterative improvement. Converts ambiguous operational needs into prioritized product backlogs, model success criteria, and maintainable designs. Partners with BI developers, data scientists, platform engineers, domain experts, and executives, explaining tradeoffs, confidence intervals, limitations, and business value in plain language. Brings practical focus on responsible AI, reliable delivery, and measurable decisions that save time and improve operational performance. EXPERIENCE AI Engineer Northstar Manufacturing Analytics January 2025 - Present Cincinnati, OH Leads production AI product delivery for plant scheduling, aftermarket operations, commercial workflows, and maintenance intelligence. Guides engineers across AWS, Databricks, Python, LLM applications, APIs, MLOps, and responsible AI, connecting technical decisions with faster operational decisions and dependable user adoption. Translated planner interviews and executive goals into a prioritized AI/ML backlog with acceptance criteria, delivery estimates, and business measures for scheduling and aftermarket decisions. Built a production RAG assistant with Python, FastAPI, OpenSearch vector search, and AWS services, giving service analysts cited contract and maintenance guidance with clear confidence limits. Created an MCP server for approved inventory, work-order, and forecast capabilities, adding authentication, versioning, rate limiting , and API documentation for reusable enterprise integration. Implemented MLflow and Weights & Biases tracking, registry promotion, automated evaluation, and A/B testing for forecasting and LLM releases across development and production. Designed Databricks medallion pipelines for demand, supplier, and work-order data, adding freshness checks, feature validation, feature engineering , and drift alerts for dependable daily outputs. Mentored four engineers on prompt engineering , SHAP, LIME, secure coding, CI/CD, automated testing, and responsible AI reviews; presented model tradeoffs and uncertainty to business leaders. Machine Learning Engineer BlueRidge Industrial Software May 2023 - December 2024 Louisville, KY Developed and deployed machine learning products for equipment service, parts demand, repair volume, and operational dashboards. Combined modeling, API engineering, deployment controls, and stakeholder discovery so manufacturing and supply chain teams could act on predictions with greater confidence and less manual analysis. Developed supervised classification and anomaly detection models for equipment service events with Python, scikit-learn, Databricks, and structured feature engineering, supporting faster maintenance triage. Delivered time-series forecasting pipelines for parts demand and repair volume, comparing seasonal baselines with gradient-boosting methods and documenting accuracy by product family. Exposed inference through versioned Flask and FastAPI REST APIs with token authentication, request validation, latency logging, OpenAPI documentation, and controlled rollback procedures. Established MLflow model registry conventions and GitHub Actions deployment checks covering unit tests, data-contract validation, container security scans, and AWS release approvals. Partnered with BI developers on predictive recommendations and natural-language search inside operational dashboards, using feedback to revise prompts and improve retrieval quality. Presented proof-of-concepts to manufacturing and supply chain stakeholders, linking prediction quality, response time, and adoption with labor planning and inventory decisions. Associate Data Scientist Riverbend Operations Lab June 2022 - April 2023 Indianapolis, IN Supported early AI/ML product discovery for production, supplier, and maintenance operations. Built reproducible analyses, data-quality controls, semantic search prototypes, and model comparisons that helped domain experts select credible use cases and move promising concepts into production-readiness review. Analyzed production, supplier, and maintenance datasets to frame anomaly detection and demand forecasting use cases, documenting assumptions, data gaps, and expected operational value. Built reproducible Python notebooks and Databricks jobs for supervised learning , clustering, time-series features, and model comparison with controlled train-test splits. Added data-quality checks for missing timestamps, duplicate work orders, inconsistent equipment identifiers, and stale source records before training data reached analysts. Prototyped semantic search with embedded maintenance documents and a vector database, evaluating retrieval precision against labeled technician questions. Used SHAP explanations and error analysis to identify influential features and communicate model limitations, confidence intervals, and false-positive patterns to domain experts. Supported Agile planning, technical documentation, peer code reviews, and live demonstrations that moved two proof-of-concepts into formal production-readiness assessments. PROJECTS Operations RAG Knowledge Service 2025 Created a reusable retrieval-augmented generation service for maintenance and contract knowledge, combining document ingestion, vector search, prompt templates, source citations, response evaluation, and API access controls. Supply Chain Forecast Evaluation Lab 2024 Built a comparative forecasting framework for parts demand and repair volume, pairing seasonal baselines with machine learning models, experiment tracking, error analysis, and business-facing accuracy reports. Predictive Maintenance Search Prototype 2023 Developed a semantic search prototype for technician questions using embedded maintenance documents, vector database retrieval, labeled evaluation data, and analysis of precision, latency, and response relevance. LEADERSHIP & AWARDS Applied AI Capstone Showcase, Best Operations Analytics Project, 2022 Cincinnati Data Science Challenge, Finalist, 2021 College of Engineering Dean's List, 2021 and 2022 EDUCATION Bachelor's Degree in Computer Science University of Cincinnati GPA: 3.8 2022 Cincinnati, OH Coursework: Machine Learning, Data Structures, Database Systems, Software Engineering, Statistics CERTIFICATIONS AWS Certified Machine Learning - Specialty 2025 Databricks Certified Machine Learning Associate 2024 DeepLearning.AI Generative AI with Large Language Models 2024 TECHNICAL SKILLS Programming Languages: Python, Java, TypeScript API Development: FastAPI, Flask, REST APIs Cloud Platforms: AWS, Amazon S3, AWS Lambda Data Platforms: Databricks, Delta Lake, SQL ML Frameworks: scikit-learn, pandas, NumPy LLM Applications: Prompt engineering, MCP servers, intelligent agents Retrieval Systems: RAG, OpenSearch, semantic search MLOps Tools: MLflow, Weights & Biases, model registries Delivery Engineering: GitHub, GitHub Actions, CI/CD Modeling Methods: Classification, clustering, optimization Forecasting Methods: Time-series forecasting, seasonal baselines, gradient boosting Model Quality: A/B testing, automated evaluation, train-test splits Observability: Model monitoring, data drift, prediction latency Responsible AI: SHAP, LIME, bias detection Software Practices: Lean, Agile, XP, automated testing SKILLS Python Java TypeScript SQL FastAPI Flask REST APIs AWS Databricks MLflow Prompt engineering RAG Vector databases Anomaly detection A/B testing PROFESSIONAL AFFILIATIONS AI and Data Science Student Association, Technical Workshop Lead, 2021 - 2022 Cincinnati Open Source Hackathon, Manufacturing Analytics Mentor, 2022 Computer Science Peer Tutor, University of Cincinnati, 2020 - 2022 LANGUAGES English (Native) Spanish (Intermediate) ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. REFERENCES AVAILABLE ON REQUEST

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