WELCOME
My name is Chris S. Murphy, I have 4+ years of Enterprise Sales in Cloud & AI technologies,
closing deals across North America, the EU, and APAC.
I'm certified in AI Product Management & AI in BioTechnology from MIT.
I consulted for a 2016 US Presidential Campaign and was 1st to train the
Kikuyu & Luganda LLM's for 15 million Kenyan speakers in 2019.
Since then, I've become highly fluent in designing Compound AI Systems,
AI Search systems, Fine-Tuning Models, Reinforcement Learning & more.
My current passion is Genomic Language Models, DNA Models and Single Cell Models and the systems around Biological Neural Networks.
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​I'm looking for a position as an Account Executive or AI Product Manager.
I've been highly successful selling Multi-Cloud &
AI SaaS Platforms to Fortune 500 companies that include Kafka, Redis, PostgreSQL, Flink, MySQL, Grafana and more.
I know how to combine traditional cloud computing lwith the new AI Stack to design and build AI applications
that actually produce ROI at a low cost.
I'm fluent across the AI Stack from Pre-Training models to Fine-Tuning, AI Search, Reinforcement Learning on Agents, Edge AI, Multimodal AI, and much more.
About Me
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Our Best Work Thus Far
In early 2025, I was inspired by Stanford's release of
EVO 2, an AI Model trained across the entire genome and it's potential for new possibilites in synthetic biology. During the spring, I worked nightly to design a
Compound AI System around EVO 2 to develop a personalized mRNA Vaccine Platform for any cancer type that works to predict cancer's immune escape and vaccinate against it.
I've presented the research at Harvard and will be donating our code to the University at the end of 2025.
Check out our Deck for the Project Here!
FEATURED AI PROJECTS
Cloud & AutoScaling AI Pipelines that use
Parameter Efficient Fine-Tuning to Fine-Tune multiple AI Models simultaneously with the same data and find the highest performing model to then push that model to your inference container.
We then monitor for data drift and retrigger fine-tuning jobs based on continuous metrics.
User/Application uses an Agent to send a personalized email to somebody based on the users personal preferences.
The Agent works with a Feature Store to pull the users features and preferences, consults a Vector Store to pull information based on the preferences, then sends them to the LLM Generator to construct the personalized email and send it to the user.
This Advanced RAG solution combines RAG with WebSearch to deliver advanced results. We use callbacks to deal with hallucinations or missing retrieval results. When using a
Tree-Based Graph with callbacks you're able to use your Vector Store in combination with Web Search to deliver outstanding RAG Results without hallucinations.
We build a Multi-Agent Workflow using different AI Models for different agentic tasks. Agents pass messages using Pydantic and have access to RAG Vector Stores & Feature Stores.
Once the Agent Tasks are completed, information is sent to an LLM Ensemble to generate an ensemble answer for relevancy.
For Ensembling, we use the same AI Inference Stack and Server as Amazon, Doordash, and Amazon.
This Multimodal AI architecture on Azure uses Agents with Memory to interact with Multimodal cloud services to convert text to images & audio to text. These services call our data-stores to pull and push transformed data back and forth for the User. This allows the user to transcribe financial
audio data into text and then summarize for use.
Climate Hub
My first company, launched in 2023.
Climate Hub is a SaaS Marketplace built on AWS using GraphQL API's, Multi-Table DynamoDB, OAuth, BM25 Search & more.
Climate Hub is a B2B & B2C Marketplace that connects buyers & sellers of Renewable Energy Products & Services.
We're at www.Climate-Hub.io!
UX for Generative AI
Applications
Deep Learning Recommendation Systems
LLM & AI Inference at Scale
Parameter Efficient
Fine-Tuning
RECENT NEWS & CHRIS BLOG
5/14/2024
GPT-4o
GPT-4o (“o” for “omni”) is a step towards much more natural human-computer interaction—it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs.
05/09/2024
The Rise of Anyscale
By 2016 & 2017, I was heavily invested in learning about technologies in the Apache OpenSource ecosystem, primarily because it had cristened and spawned so many incredible tech companies up to that point
5/08/2024
Mutli-Document Agentic RAG