NMNick McCarthy

Nick McCarthy

Senior Specialist Solutions Architect, Generative AI

NYC, USA

Worldwide Tech Lead for OpenAI on Amazon Bedrock

Leading go-to-market, technical enablement, launch readiness, and solution architecture guidance for OpenAI on Amazon Bedrock.

+6Years ExperienceBScPhysicsMScMachine Learning
Nick McCarthy

About

I am a Senior Specialist Solutions Architect in the Amazon Bedrock team at AWS and the worldwide technical lead for OpenAI on Amazon Bedrock — leading go-to-market, launch readiness, and solution architecture guidance, and acting as the highest point of technical escalation for customers adopting OpenAI models and Codex on Bedrock.

Most of my work sits between product, field, and implementation: helping teams understand what to build, helping SAs explain it clearly, and turning model customization patterns into practical guidance customers can use. That includes founding the OpenAI Technical Field Community and running train-the-trainer bootcamps in New York, Seattle, and London that now seed twelve regional bootcamps across NAMER and EMEA.

I also work closely with the Amazon Bedrock Applied Science team on model customization and reinforcement learning launches, translating new capabilities into field-ready technical guidance, benchmarking, and customer patterns — and presenting that work at AWS Summits, re:Invent, and re:Inforce.

Before Bedrock, I worked in AWS Professional Services on LLMOps, SageMaker AI platforms, explainability, and applied ML delivery for customers including Booking.com, AstraZeneca, BPX Energy, Ericsson, and Siemens Mendix. My background also includes reinforcement learning for finance and UCL degrees in Physics and Machine Learning.

This site collects that trail: AWS technical writing, selected research, education, talks, and project notes with links close to the claims they support.

Timeline

Professional and education milestones, with skills and related artifacts in each dropdown.

ProfessionalEducation

Skills & technologies used

Amazon BedrockAmazon SageMaker AIAmazon Bedrock Custom Model ImportAmazon Bedrock Reinforcement Fine-TuningModel customization workflowsLLMOpsReinforcement learning with verifiable rewards (RLVR)OpenAI-compatible APIs
Amazon Bedrock RFT workflowBedrock Custom Model Import performance chartsVLM document-to-JSON fine-tuning workflowMulti-provider GenAI gateway architecture
  • Worldwide technical lead for OpenAI on Amazon Bedrock, driving a multi-billion dollar annual revenue target — aligning OpenAI, the Bedrock service team, and the worldwide SA community as the highest point of technical escalation to unblock customers and accelerate adoption.
  • Leading the OpenAI Technical Field Community and Champions Group to enable 1,000 field SAs — designing and delivering three two-day train-the-trainer bootcamps in New York, Seattle, and London covering OpenAI models on Bedrock and OpenAI Codex enterprise deployment; graduates now run 12 local, geo-specific bootcamps, scaling enablement across NAMER and EMEA ahead of GA.
  • Drove 29 influenced opportunities worth $16M in ARR, 11,326 registrations, and ~30 lighthouse enterprise customers through the AWS AI League go-to-market — by building hands-on model-customization challenges, customer GTM assets, launch workshops at the AWS NYC Summit and re:Invent, a 16-company hackathon across five time zones, and the Atos customer story.
  • Shaped the launch readiness of SageMaker AI's serverless, agentic model-customization capability as a key scientific advisor — contributing RLVR workflow design, HyperPod training recipes, a penetration-testing playbook, Robin AI preview support, and a 30-person cross-org preview program — by partnering with the Amazon Bedrock Applied Science team to turn new capabilities into field-ready guidance.
  • Published a reusable LLMOps fine-tuning platform (the Model Customization Platform Accelerator) to AWS Samples and delivered it as a re:Invent Builder's Session and AWS North America Tech Summit session — giving customers and field teams a production-ready blueprint for fine-tuning on SageMaker AI.
  • Drove Talent.com's foundation-model adoption decision and co-authored AWS's reference guidance on a mature GenAI foundation — including the Generative AI Lens multi-tenant scenario and model-customization readiness frameworks — by running tailored SageMaker AI and Bedrock workshops adopted across customer engagements.
  • Built the internal OpenAI and Anthropic model parity dashboards used by the Bedrock field — running load testing and performance benchmarking across the model families and Codex so SAs can position Bedrock deployments against first-party provider offerings with real data.
  • Driving the AI-DLC (AI-Driven Development Life Cycle) with Codex workstream — applying AWS's AI-DLC methodology to OpenAI Codex on Bedrock for enterprise agentic development, with a joint blog post publishing soon.
  • Presented joint AWS and OpenAI breakout sessions at the AWS New York and Los Angeles Summits on OpenAI models and Codex on Amazon Bedrock, a 250-person AWS London Summit breakout, and a re:Inforce chalk talk, and led a six-person re:Invent Builder's Session team for an LLMOps platform build-out.
  • Turned launch collaborations into public, field-ready guidance — publishing reinforcement fine-tuning on Amazon Bedrock walkthroughs informed by RLVR preview work with customers like Robin AI, VLM fine-tuning for document-to-JSON workflows, Bedrock Custom Model Import, and multi-provider GenAI gateways.

Skills & technologies used

LLMOpsAmazon SageMaker AIAmazon BedrockAWS TrainiumAWS InferentiaResponsible AIModel evaluationFine-tuning workflows
Time series forecasting with Amazon SageMaker AutoMLBMW cloud efficiency analytics dashboard
  • Led delivery of an end-to-end LLMOps platform for Booking.com — building automated fine-tuning, evaluation, load-testing, and deployment workflows on Amazon SageMaker that gave their ML teams a repeatable path from experiment to production for open-source LLMs.
  • Took Booking.com's AI Trip Planner to production, serving 100M+ app users — by fine-tuning open-source LLMs for destination recommendations with the AWS Generative AI Innovation Center, reducing the product's dependency on proprietary OpenAI models and giving Booking.com control over cost, latency, and model behavior.
  • Led the winning team at the Aviva GenAI Hackathon 2023 as sole technical advisor and ML engineer — designing and building AutoClaim, an automated insurance-claim bot powered by Amazon Bedrock and Amazon Kendra.
  • Defined go-to-market strategy and scoped GenAI engagements for major energy enterprises including TotalEnergies, ENI, and BPX Energy — serving as GenAI technical lead for AWS Professional Services Energy UKIR and delivering 1:1 C-level advisory through the UKIR GenAI Solutions Launchpad.
  • Shaped the roadmaps of four AWS GenAI services — Amazon SageMaker AI, Amazon Bedrock, AWS Trainium, and AWS Inferentia — by channeling enterprise customer feedback into feature requests, Responsible AI governance, and beta testing of preview features such as Bedrock Custom Model Import.
  • Published public work on time series forecasting with Amazon SageMaker AutoML and BMW cloud efficiency analytics with Amazon QuickSight and Amazon Athena.

Skills & technologies used

MLOpsAmazon SageMaker AIAmazon SageMaker ClarifyAmazon SageMaker CanvasExplainable AICI/CD pipelinesGenomics workflowsData migrationsPartner enablementPre-sales advisory
SageMaker Canvas facies classification workflowBundesliga xGoals SHAP pressure plots
  • Influenced $31M in AWS Professional Services bookings across 23 unique opportunities — through strategic pre-sales discussions with C-level stakeholders.
  • Cut model deployment time 30% and improved model accuracy 60% for BPX Energy, unlocking ~40% ROI-increase potential — by implementing an MLOps framework on Amazon SageMaker AI with automated monitoring and re-training; the platform was showcased by the BPX CTO at re:Invent 2022.
  • Cut ML use-case onboarding time 83% and saved $453K in deployment costs for Ericsson — by leading delivery of an MLOps platform on Amazon SageMaker AI that took onboarding from three days to four hours across four production ML use cases.
  • Migrated 300 production apps to Aurora Serverless v2 ahead of schedule as Lead Data Architect for Siemens Mendix — delivering 3x faster large scaling events and ~30% database cost savings, plus a product feature request adopted onto the Amazon RDS roadmap.
  • Saved €200K across 10 customer engagements for AWS partner ML6 — by designing a code-promotion CI/CD pattern that moves ML code between environments rather than promoting opaque model artifacts.
  • Trained 175+ consultants across five AWS partners — by co-founding the Partner Workshop Factory, a 19-person team that built 14 reusable workshops, leading EMEA partner ML immersion days, and organizing two GenAI hackathons for Rackspace.
  • Mentored 89 colleagues to AWS speaker certifications as a UKIR Certification Champion, coached two graduates into the ML Technical Field Community, and co-created the Artifact Acceleration Day — guiding 96 graduates to 25 published AWS guidance artifacts.
  • Cut deployment time 87% and saved AstraZeneca $490K annually plus 120 scientist-hours per year — by designing a new deployment workflow, a Genomics Data Lake, and the AI Bench MLOps platform with five SageMaker Pipeline products, enabling 300+ researchers to manage 1,200+ ML experiments annually.
  • Delivered the explainability layer for Deutsche Fussball Liga's in-production expected-goals models — designing Amazon SageMaker Clarify experiments published as an AWS ML Blog post and a German DevOps magazine feature, and surfacing a missing SHAP KernelExplainer capability as a product feature request.
  • Built AWS Dash, an internal KPI tracker used by 3,000+ consultants and managers — leading a four-person backend team on API Gateway, Lambda, Glue, and RDS.
  • Presented externally at the AWS Manchester User Group and UCISA23, and created the AI Air Hockey session at the re:Invent 2022 Builder's Fair, attended by 250 participants.

Skills & technologies used

Proximal Policy Optimization (PPO)Deep Deterministic Policy Gradient (DDPG)Soft Actor-Critic (SAC)Portfolio constructionMacroeconomic dataReinforcement learning trading environments
  • Integrated PPO, DDPG, and SAC into a trading platform that mapped investment signals to target portfolio weights.
  • Designed RL trading environments and researched neural architectures, action distributions, and macro-data state representations.
  • The resulting models delivered superior out-of-sample returns versus xAI's legacy long-only strategies and moved into production on the platform.

Skills & technologies used

Deep reinforcement learningNatural language processingPortfolio optimizationPythonAcademic research writing
Machine Learning MSc visual
Chess pieces from MIT Technology Review SentiMATE article
  • Thesis: Deep Reinforcement Learning for Portfolio Construction & Optimisation, in collaboration with xAI Asset Management.
  • Completed the "Deep Learning and Reinforcement Learning" module, taught by Google DeepMind at UCL.
  • Related public research: SentiMATE, accepted for oral presentation at AIIDE 2019 and covered by MIT Technology Review.

Skills & technologies used

Experimental physicsDoppler-free spectroscopyOpticsUncertainty analysisData analysis
UCL Wilkins Building
  • Thesis: Investigation of the hyperfine structures of Rubidium using Doppler-free, frequency-modulated saturated absorption spectroscopy.
  • Graduated with First Class Honours.

Latest Blogs

All blogs

Speaking

Selected public talks, workshops, and conference sessions across AWS events, customer forums, and research venues.

Nick McCarthy presenting on stage at the AWS New York Summit

AWS New York Summit / Jun 17, 2026 / New York, USA

Build with OpenAI Models and Codex on Amazon Bedrock

Co-presented a joint AWS and OpenAI session with Chris Dickens, OpenAI's product lead for Amazon, on building with OpenAI models and Codex on Amazon Bedrock.

Joint AWS and OpenAI breakout session at the NYC Summit.

Nick McCarthy presenting at the AWS Los Angeles Summit

AWS Los Angeles Summit / Jun 10, 2026 / Los Angeles, USA

OpenAI frontier models & Codex on Amazon Bedrock

Presented a session on the generally available OpenAI frontier models and Codex on Amazon Bedrock.

Breakout session at the LA Summit.

AWS AI League workflow

NYC Summit and AWS re:Invent / 2025 / New York / Las Vegas

AWS AI League launch workshops

Helped build hands-on model-customization challenges, customer GTM assets, service-team feedback loops, and launch workshops for AWS AI League.

11,326 registrations, roughly 30 lighthouse enterprise customers, 29 influenced opportunities / $16M ARR.

AWS re:Invent AIM319 LLMOps workshop graphic

AWS re:Invent / 2025 / Las Vegas, USA

LLMOps and SageMaker AI fine-tuning workshop

Built and delivered a hands-on LLMOps workshop around automated fine-tuning, evaluation, and deployment workflows on Amazon SageMaker AI.

50 builders, 5 instructors, and 150 GPUs across the workshop environment.

Education

UCL degrees, thesis work, and related research links.

UCL

Sept 2018 - Aug 2019

Machine Learning MSc

University College LondonDistinction (77%)

Focused on reinforcement learning, statistical learning, and practical ML systems. Related public research includes SentiMATE, accepted for oral presentation at AIIDE 2019.

Thesis:Deep Reinforcement Learning for Portfolio Construction & Optimisation, in collaboration with xAI Asset Management.

SentiMATE arXivMIT Tech Review
UCL

Sept 2014 - Jun 2017

Physics BSc

University College LondonFirst Class Honours

Built a quantitative grounding in experimental physics, optics, spectroscopy, and data analysis.

Thesis:Investigation of the hyperfine structures of Rubidium using Doppler-free, frequency-modulated saturated absorption spectroscopy.

Papers

Publication-style research work linked back to the relevant source.

Let's Connect

Open to collaborations, project conversations, and thoughtful feedback loops.

EmailLinkedInGitHub