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Hello, I'm Nicholas Marler👋

I build Generative AI and agentic systems in Python, FastAPI & AWS/GCP for healthcare and wellness.

Senior Software Engineer building AI-powered healthcare and wellness products, multi-agent workflows, and event-driven cloud platforms with AWS Bedrock, Claude, MCP, and Python. I design production systems for personalized health coaching and real-time analytics.

Syncing...Resume

Stack

Python
TypeScript
Java
Azure OpenAI
Azure AI Search
AWS Bedrock
Claude
Model Context Protocol
CrewAI
LlamaIndex
Pinecone
PydanticAI
Snowflake Cortex AI
Databricks
LangChain / LangGraph
SageMaker
FastAPI
Spring Boot
Node.js
GraphQL
React
Next.js
Tailwind CSS
Azure
AWS
Google Cloud
Terraform
Kubernetes
Docker
PostgreSQL
Redis
OpenSearch
MySQL
MongoDB
Kafka / MSK
Kong AI Gateway
New Relic

About me

I'm a Senior Software Engineer based in Austin, Texas, with a Computer Science degree from Texas Tech University. I focus on production Generative AI, agentic workflows, and cloud-native systems. At MyNetDiary, I helped build AI-powered healthcare and wellness capabilities including personalized coaching, recommendations, voice interaction, and health analytics. My work spans AWS Bedrock, Claude, MCP, LangChain, LangGraph, CrewAI, LlamaIndex, Pinecone, Python, FastAPI, and event-driven services across AWS and GCP. I care about secure, observable systems that deliver practical user outcomes.

Experience

Senior Software Engineer

MyNetDiary · 06/2022 — 07/2026

Architected AI-powered healthcare and wellness capabilities, from personalized coaching and recommendations to voice interaction and real-time health analytics.

  • Architected personalized health-coaching, meal-recommendation, exercise-planning, AI voice-interaction, notification, and digital-health-twin capabilities with AWS Bedrock, Claude, MCP, LangChain, LangGraph, CrewAI, LlamaIndex, Pinecone, and PydanticAI
  • Designed Nutrition, Exercise, Health Risk, and AI Coach multi-agent RAG workflows with semantic and vector search, memory, and tool calling, reducing manual health analysis by over 80% and improving recommendation quality
  • Built AI knowledge and retrieval systems with LlamaIndex, LangChain, Pinecone, vector embeddings, and Bedrock Knowledge Bases for nutrition, wearable, health-record, and behavioral data
  • Developed high-volume microservices with Python, FastAPI, Node.js, PostgreSQL, Redis, OpenSearch, Kafka/MSK, and Kong Gateway for health analytics, wearable integrations, AI assistants, and notifications
  • Introduced event-driven architecture and asynchronous Kafka/MSK pipelines to improve scalability, resiliency, and real-time processing
  • Engineered analytics platforms with Databricks, Snowflake Cortex AI, feature engineering, vector processing, and real-time analytics for health insights and personalized recommendations
  • Deployed secure AWS and GCP infrastructure with Kubernetes (EKS), Terraform, GitHub Actions, and CI/CD, reducing deployment time by approximately 70%
  • Implemented AWS Bedrock Guardrails, PII detection and masking, prompt-injection protection, encryption, audit logging, and Responsible AI controls for sensitive healthcare data
  • Instrumented New Relic observability, distributed tracing, telemetry, AI monitoring, and operational dashboards, reducing issue-resolution time by 50%

Software Engineer

Charles Schwab · 04/2020 — 06/2022

Engineered enterprise backend services and APIs for brokerage systems, focused on latency, throughput, and operational reliability.

  • Engineered enterprise backend applications with Python, Java, and Spring Boot, reducing average request latency by 15% while roughly doubling transaction-processing capacity
  • Delivered RESTful and GraphQL APIs and microservices with established integration and security practices, reducing internal integration time by 30%
  • Optimized PostgreSQL data models, SQL queries, and access patterns, improving query performance by 25%
  • Deployed cloud applications on AWS EC2, RDS, S3, and Lambda, raising system availability to 99.9% and reducing provisioning time by 40%
  • Refactored backend processing through concurrency optimization and application profiling, reducing CPU utilization by ~20%
  • Automated build, deployment, and release workflows with scripting and CI/CD tooling, reducing manual deployment effort

Software Developer

General Motors · 03/2019 — 04/2020

Delivered full-stack enterprise CRM applications on Azure, from React and Next.js interfaces through FastAPI microservices and event-driven backends.

  • Built customer-facing and internal CRM workflows with React.js, Next.js, TypeScript, JavaScript, Tailwind CSS, Node.js, and Python/FastAPI, reducing manual data entry by 25%
  • Connected React components to Node.js APIs and Azure services for CRM pages and business workflows
  • Implemented scalable FastAPI microservices and APIs integrating CRM functionality with enterprise systems
  • Optimized PostgreSQL schemas and SQL queries, reducing average query latency by 15%
  • Deployed applications through Azure App Service and Azure Kubernetes Service (AKS), with Apache Kafka event-driven communication
  • Partnered with product, QA, and engineering teams on testing, troubleshooting, Azure DevOps and Bamboo deployment pipelines, and technical documentation

Frontend Developer

L3Harris Technologies · 01/2018 — 02/2019

Built responsive frontend applications and progressive web experiences for internal and customer-facing platforms.

  • Created responsive AngularJS and TypeScript applications, reducing user-error reports by 40% and increasing adoption by 30%
  • Built progressive web applications with React, Next.js, and Tailwind CSS for improved performance and cross-device usability
  • Integrated Swagger-documented REST APIs and added automated frontend tests to improve reliability and maintainability
  • Collaborated in Agile sprints on code reviews, Git workflows, and iterative feature delivery

How I work

  • Architecture First: Requirements translated into a design before code. No feature creep.
  • Measurable Outcomes: Latency, relevance, and cost targets defined up front, then proven.
  • Evaluated & Guarded: LLM output measured for relevance, faithfulness, and citation accuracy before ship.
  • Cross-Functional: Close partnership with product, architecture, and QA across the SDLC.

What I can help you with

  • RAG and multi-agent workflows with AWS Bedrock, MCP, LangChain, LangGraph, LlamaIndex, and Pinecone.
  • Production AI applications with evaluation, prompt-injection protection, PII controls, and Responsible AI guardrails.
  • Cloud-native, event-driven backends with Python, FastAPI, Kafka, Kubernetes, and AWS/GCP.

Certifications

Microsoft Specialist: Architecting Microsoft Azure Solutions

microsoft • July 2016

Microsoft Certified Professional

microsoft • July 2016

Let's talk

I'm seeking my next opportunity to apply AI and full-stack expertise to high-impact, reliable products. Reach out if you'd like to connect.

LinkedIn@nicholas-marler