Applied AI/ML Engineer & MLOps Expert

Alex James Watson/ https://www.linkedin.com/in/alex-watson-570a2a/

Applied AI/ML Engineer MLOps Expert

Location: Raleigh, North Carolina Mobile: +1 (919) 378- 0363 *************@*****.*** Professional Summary:

Results-driven Applied AI/ML Engineer and Software Architect with 13+ years of experience designing, developing, and deploying enterprise-scale Artificial Intelligence, Machine Learning, Generative AI, and cloud-native software solutions across technology, healthcare, life sciences, and manufacturing industries. Proven expertise in building production-grade Machine Learning, Deep Learning, NLP, Computer Vision, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and Agentic AI solutions that drive business transformation, intelligent automation, and predictive decision-making. Extensive experience architecting and deploying scalable AI platforms using OpenAI, Azure OpenAI, Claude, Gemini, Llama, TensorFlow, PyTorch, Scikit-learn, LangChain, LlamaIndex, Pinecone, FAISS, Weaviate, and MLflow. Strong background in MLOps, AI Governance, Responsible AI, Model Fine-Tuning, Feature Engineering, Data Engineering, Cloud Computing, and Distributed Systems, with hands-on expertise across AWS, Azure, and Google Cloud Platform (GCP). Demonstrated success leading cross-functional teams, developing AI-powered products, implementing enterprise-grade ML pipelines, and delivering high-impact solutions that improve operational efficiency, forecasting accuracy, customer engagement, and business intelligence. Skilled in designing end-to-end architectures spanning AI/ML, backend engineering, data platforms, DevOps, Kubernetes, microservices, and modern frontend technologies. Adept at translating complex business requirements into scalable, secure, and compliant AI-driven solutions while ensuring model reliability, explainability, governance, and regulatory compliance.

Holds a Ph.D. in Information and a Master of Applied Data Science from the University Of Michigan School Of Information, combining deep academic research with extensive industry experience to deliver innovative AI solutions that create measurable business value.

Professional Experience:

HP Inc. USA April 2024- Present

Applied AI/ML Engineer

• Design and deploy enterprise-scale Machine Learning and Deep Learning models supporting predictive analytics, intelligent automation, and AI-driven business solutions.

• Develop supervised and unsupervised learning models using PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM.

• Build advanced NLP and Computer Vision solutions that improve business process efficiency and customer engagement.

• Architect and implement enterprise Generative AI platforms utilizing OpenAI, Azure OpenAI, Claude, Gemini, and Llama models.

• Develop Retrieval-Augmented Generation (RAG) applications using LangChain, LlamaIndex, Pinecone, FAISS, Weaviate, and ChromaDB.

• Fine-tune Large Language Models (LLMs) to improve domain-specific accuracy, response quality, and contextual understanding.

• Engineer AI Agents and Agentic AI workflows for intelligent task automation and decision support.

• Build scalable ETL pipelines and feature engineering workflows leveraging Apache Spark and Databricks.

• Manage large-scale structured and unstructured datasets for AI training, evaluation, and deployment.

• Implement data quality validation frameworks to ensure reliable AI model performance.

• Establish end-to-end MLOps pipelines utilizing MLflow, Kubeflow, Airflow, Docker, Kubernetes, and GitHub Actions.

• Automate model deployment, monitoring, retraining, and version management processes.

• Deploy AI solutions on Azure Machine Learning, AWS, and GCP cloud environments.

• Implement Explainable AI (XAI), AI governance, model fairness, privacy, and security controls.

• Collaborate with stakeholders to ensure compliance with enterprise Responsible AI standards. Bruker Morrisville, North Carolina August 2021- March 2024 Principal Software Engineer

• Led the design and implementation of an AI-driven returns and quality intelligence platform used by pharmaceutical and life- sciences teams to analyze instrument returns, failure modes, and compliance documentation.

• Built production ML pipelines using PyTorch and Scikit-learn to classify return reasons and predict downstream quality risk, improving returns analysis accuracy by 15% and reducing manual review effort by ~70%.

• Introduced a multi-agent GenAI architecture where specialized agents handled document ingestion, semantic retrieval, risk summarization, and compliance validation, enabling modular reasoning instead of monolithic prompts.

• Implemented Model Context Protocol (MCP) to standardize how agents accessed instrument data, SOPs, service histories, and policy constraints, ensuring consistent context boundaries across workflows.

• Fine-tuned LLMs on internal returns documentation using AWS Bedrock and deployed via SageMaker pipelines with versioning, rollback, and human-approval gates for regulated use.

• Delivered Angular dashboards exposing agent-generated insights, confidence scores, and source references, driving a 30% increase in adoption across quality and operations teams.

• Architected predictive supply-chain and inventory forecasting systems supporting Bruker’s global manufacturing and distribution operations under demand volatility and regulatory constraints.

• Designed a multi-agent decision-support workflow where forecasting agents, constraint-checking agents, and recommendation agents collaborated to propose inventory actions without executing changes autonomously.

• Used MCP-based context contracts to enforce clear separation between raw predictions, business rules, and recommended actions, preventing agents from over-stepping authorization boundaries.

• Built automated ML workflows using Scikit-learn and deep learning models, eliminating ~70% of manual forecasting work and improving operational efficiency by 25%.

• Deployed high-concurrency prediction APIs in Go and FastAPI with 99% reliability, backed by optimized PostgreSQL schemas and Kafka-driven event ingestion for near-real-time updates.

• Orchestrated agent execution and inference pipelines using Kubeflow, Azure Functions, and GCP Cloud Functions with Redis caching, reducing infrastructure costs by ~20% while maintaining low latency.

• Integrated Azure Machine Learning and GCP BigQuery for large-scale training and evaluation, achieving a 12% improvement in forecast accuracy in production conditions.

• Built React/TypeScript dashboards that surfaced agent recommendations, rationales, and alternative scenarios, improving inventory tracking precision by 35% and increasing planner trust in AI outputs. Apple Doral, Florida July 2020 – July 2021

Senior Software Engineer / Technical Architect

• Acted as a technical bridge between data science, backend, and frontend teams, helping integrate early ML and AI-driven analytics into existing production systems.

• Deployed ML models using Azure Machine Learning and MLflow, establishing repeatable experiment tracking and controlled rollout of AI-driven features in enterprise environments.

• Implemented caching and orchestration layers using Redis and Kubeflow, enabling low-latency inference and reliable model updates without service disruption.

• Designed and built full-stack applications using React, TypeScript, Next.js, and Redux, delivering userfriendly, data-heavy interfaces that reduced manual inventory tracking effort by ~40%.

• Implemented secure backend services using Go, GraphQL, and Node.js, improving API throughput and reducing average query latency by ~8ms while maintaining strict access controls.

• Integrated large-scale ETL pipelines using GCP Dataflow and Snowflake, enabling near–real-time analytics and improving operational visibility by ~18%.

• Led the migration of legacy services to AWS, containerizing workloads with Docker and Kubernetes to achieve 99.9% uptime while reducing infrastructure costs by ~20%.

• Built automated testing frameworks in Python and Node.js, integrating GitHub Actions–based CI/CD pipelines that accelerated QA cycles by ~10% and improved delivery predictability.

• Optimized hybrid CI/CD workflows across GCP Cloud Build and Azure DevOps, improving deployment speed by ~15% for full-stack and ML-enabled services.

• Supported early GenAI-style workflows using TensorFlow-based models and prompt-driven analysis, focusing on explainability, bias detection, and safe integration rather than autonomous decision-making.

• Regularly balanced architectural tradeoffs across performance, security, and maintainability while managing three concurrent projects in a fast-moving, high-visibility environment. IQVIA Durham, North Carolina November 2018- July 2020 Senior Software Engineer

• Built and maintained enterprise healthcare web applications using React, TypeScript, Next.js, and Redux, delivering secure, data-heavy interfaces that reduced manual reporting and data handling effort by ~40%.

• Engineered scalable backend services with Go, Node.js, and GraphQL, improving API throughput and increasing data processing performance by ~15% while reducing average query latency by ~8ms.

• Designed and integrated cloud-based data pipelines using Snowflake and GCP Dataflow, enabling reliable ETL workflows and improving analytics visibility across internal and client-facing tools by ~18%.

• Led the migration of legacy systems to AWS, containerizing services with Docker and Kubernetes to achieve 99.9% uptime while reducing infrastructure costs by ~20%.

• Implemented automated testing frameworks in Python and Node.js and integrated GitHub Actions CI/CD, accelerating QA cycles and improving delivery predictability by ~10%.

• Collaborated with data science teams to integrate lightweight ML and TensorFlow-based analytics into production workflows, focusing on safe inference, monitoring, and explainable outputs rather than autonomous decisioning.

• Unified hybrid CI/CD pipelines across GCP Cloud Build and Azure DevOps, improving deployment speed by ~15% while supporting simultaneous full-stack and ML-enabled services. BBI Durham, North Carolina February 2013- November 2018 DevOps Engineer/ Full Stack Engineer

• Built and evolved full-stack web applications using Angular, JavaScript, Node.js, Java (Spring Boot), Hibernate/JPA, and MySQL, delivering 25+ production systems and custom components across multiple client engagements.

• Designed and maintained modern CI/CD pipelines using AWS Developer Tools (CodeCommit, CodeBuild, CodePipeline), reducing release cycle times by ~40% and improving deployment reliability.

• Containerized applications with Docker and managed image distribution via Docker Hub and AWS ECR, improving consistency across environments and reducing deployment-related issues by ~25%.

• Implemented Kafka-based RESTful and event-driven APIs for real-time data streaming, reducing message processing latency by ~25% and improving system responsiveness.

• Authored backend services and ETL workflows in Python (Django/Flask), enabling faster data processing and delivering actionable insights ~25% faster to 15+ enterprise clients.

• Built and operated Kubernetes-orchestrated microservices in Go, integrating third-party APIs and improving service reliability while reducing production bug rates by ~80%.

• Maintained 99.9% uptime during high-risk content updates and database migrations by applying disciplined DevOps practices across AWS infrastructure, monitoring, and rollback strategies. Technical Skills:

• Applied AI & Machine Learning: Production ML systems, TensorFlow, PyTorch, Hugging Face Transformers, Scikitlearn, NLP, Generative AI, RAG architectures, LangChain, Computer Vision, Statistical Modeling, AutoML, Model Optimization, Model Serving (Triton Inference Server, ONNX Runtime)

• Data Engineering & Databases: Data warehousing, ETL pipelines, streaming and batch processing, feature engineering, data modeling and schema design, PostgreSQL, MongoDB, Redis, GCP BigQuery, vector databases (Weaviate, Milvus), data encryption and secure data handling

• Cloud Platforms & MLOps: WS (SageMaker, Bedrock), GCP (Vertex AI, Cloud Run, BigQuery), Azure (Cognitive Services, Functions), cloud-native ML workflows, serverless architectures, infrastructure as code, API gateways, monitoring and observability (Prometheus, Grafana)

• Backend Engineering & APIs: Python (FastAPI), Go (Golang), Node.js (Express), RESTful APIs, GraphQL, API design and versioning, authentication and authorization, performance tuning, concurrency patterns, event-driven systems

• Frontend Engineering: React, TypeScript, Redux, Next.js, Angular, Material UI, Tailwind CSS, Context API, interactive SPA development, real-time data visualization

• API & Backend Technologies: RESTful APIs, GraphQL, Node.js (Express), Go (Golang), Python (FastAPI), API Design, API Gateways, Authentication, Authorization, Performance Tuning, Concurrency Patterns

• Frontend Technologies: React, Redux, TypeScript, Next.js, Angular, Material UI, Tailwind CSS, Context API, Interactive Frontend Components, Single Page Applications (SPAs)

• DevOps & Platform Engineering: Docker, Kubernetes, Helm, CI/CD pipelines (GitHub Actions), container orchestration, serverless deployments, infrastructure automation, logging and monitoring (ELK stack, Prometheus, Grafana)

• Security & Compliance: HIPAA-compliant system design, access control and identity management, encryption in transit and at rest, AWS/GCP/Azure security services, vulnerability scanning, penetration testing, security audits Educations:

• Doctor of Philosophy (Ph.D.) in Information Technology- University of Michigan School of Information

• Master of Applied Data Science- University of Michigan School of Information

• Bachelor of Science (B.S.) in Computer Science - East Carolina University Certifications:

Certification Issuing Organization Credential ID

Introduction to Neural Networks and PyTorch IBM G3QT5S6XHVRM Applied Machine Learning in Python University of Michigan – School of Information

NFKJQU3V3GPN

Neural Networks and Deep Learning DeepLearning.AI CXJLPAFG9RT8

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