NMNick McCarthy

Experience

Professional and education timeline.

Each entry expands with skills, related artifacts, and the context behind the role.

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

Skills & technologies used

LLMOpsAmazon SageMaker AIAmazon BedrockAWS TrainiumAWS InferentiaResponsible AIModel evaluationFine-tuning workflows
Time series forecasting with Amazon SageMaker AutoMLBMW cloud efficiency analytics dashboard

Skills & technologies used

MLOpsAmazon SageMaker AIAmazon SageMaker ClarifyAmazon SageMaker CanvasExplainable AICI/CD pipelinesGenomics workflowsAWS LambdaAmazon API GatewayAmazon RDSAWS GlueAmazon QuickSight
SageMaker Canvas facies classification workflowBundesliga xGoals SHAP pressure plots
  • Implemented an MLOps framework for BPX Energy on Amazon SageMaker AI that reduced deployment time and improved model accuracy.
  • Led an MLOps platform for Ericsson that reduced onboarding time across four ML use cases and avoided significant deployment cost.
  • Designed a code-promotion CI/CD pattern for AWS partner ML6 that moved ML code between environments rather than promoting opaque model artifacts.
  • Presented externally at the AWS Manchester User Group and UCISA23, and led EMEA partner ML immersion days.
  • Influenced 23 AWS Professional Services opportunities worth $31M in bookings through strategic pre-sales discussions with C-level stakeholders.
  • Designed Amazon SageMaker Clarify experiments for Deutsche Fussball Liga expected-goals models, resulting in an AWS ML Blog post.
  • Built AstraZeneca deployment and genomics workflows that reduced deployment time and saved scientist hours.
  • Delivered AI Bench on Amazon SageMaker AI, enabling 300+ researchers to manage over 1,200 ML experiments annually.
  • Led backend delivery for AWS Dash, an internal KPI tracker using API Gateway, Lambda, Glue, and RDS.
  • Created and delivered 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.

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.