Principal Machine Learning Engineer
Principal ML Engineer with a Master's in Data Science and Machine Learning and 10+ years of software engineering. I design production AI and MLOps systems — generative AI, language models, vector search, and secure cloud platforms on AWS, Azure, and GCP.
Production AI, MLOps, and engineering platforms I've shipped with: LLMs, vector databases, cloud ML, and infrastructure that runs at scale.
Transformers, GANs, and VAEs for synthetic data, summarization, and anomaly detection in production workflows.
Pinecone, embeddings, hybrid search, multi-vector retrieval, and chunking tuned for relevance at query time.
SageMaker, Azure Machine Learning, Databricks, and Terraform-backed pipelines from training through deploy.
AWS, Azure, and GCP for scalable ML services, identity, and infrastructure as code.
Docker, Kubernetes, Spark, and Azure DevOps for secure, repeatable ML and software delivery.
Quantization, Matryoshka Representation Learning, vector weighting, and cross-field search.
Lifecycle control, attestation, and policy-aligned deployment mapped to NIST AI RMF and EU AI Act concepts.
Asset optimization, annotation, model development, and visualization for high-stakes operational data.
Principal ML engineer who ships generative AI, MLOps, and production software — from vector search to governed model runtimes across cloud and on-prem.
Hello, I'm Peter Henshaw. As a Principal Machine Learning Engineer, I use my Master's in Data Science and Machine Learning and more than a decade of software engineering to design solutions for asset optimization and predictive analytics. I specialize in MLOps, generative AI, language models, and vector databases, with tools such as Pinecone, SageMaker, Azure Machine Learning, Terraform, Docker, Kubernetes, Spark, Azure DevOps, and Databricks.
I work with data scientists, engineers, and analysts to deliver scalable, secure ML pipelines — including RAG on AWS, Azure, and GCP. I apply GANs, VAEs, and Transformers for synthetic data, summarization, and anomaly detection, and I prototype AI-lifecycle controls so models run only when they are authorized, audited, and policy-compliant.
GenAI, LLMs, RAG, AI governance
MLOps, SageMaker, Azure ML, Databricks
10+ years building production systems
AWS, Azure, GCP, K8s, Terraform
Pinecone, embeddings, hybrid retrieval
IAM, attestation, lifecycle control
Principal-level AI, machine learning, and software engineering — production MLOps, retrieval systems, and multi-cloud platforms.
Language models, Transformers, GANs, and VAEs for search, synthetic data, summarization, and anomaly detection — designed for real operational use cases, not demos.
End-to-end ML pipelines on SageMaker, Azure ML, and Databricks — training, packaging, and deploying models with Terraform, Docker, and Kubernetes.
Ingestion to Pinecone, chunking, embeddings, quantization, hybrid and multi-vector search, and retrieval weighting that improves relevance at query time.
A decade of production software across financial services and industrial AI — identity, cloud-native services, and systems teams can operate with confidence.
Cross-cloud IAM, ML platforms, and infrastructure as code so AI workloads stay portable, secure, and recoverable across providers.
Hardware-rooted trust, remote attestation, and time-bound execution licenses so models can be renewed, revoked, or safely degraded under policy.
Principal ML, AI consulting, and software engineering roles spanning energy, industrial intelligence, and financial services.
FirstEnergy · Full-time
United States
Lead machine learning and AI engineering for production systems — model development, MLOps, and software that turns operational data into predictive insight.
Partner with engineering and data teams to ship secure, scalable ML services that other engineers can operate, audit, and extend.
Hexagon Asset Lifecycle Intelligence · Full-time
Asset optimization · Predictive analytics
Design and implement ML solutions for asset optimization and predictive analytics, specializing in MLOps, generative AI, language models, and vector databases with Pinecone, SageMaker, Azure Machine Learning, Terraform, Docker, Kubernetes, Spark, Azure DevOps, and Databricks.
Deliver RAG and MLOps on AWS, GCP, and Azure: ingestion to vector stores, embedding models, hybrid and multi-vector search, quantization, and Matryoshka Representation Learning. Apply GANs, VAEs, and Transformers for synthetic data, summarization, and anomaly detection.
Charles Schwab · Full-time
Texas, United States
Senior software engineering across cloud identity and access: AWS IAM, AWS Resource Access Manager, Azure AD, Conditional Access, and Azure B2B — security that production ML and application platforms depend on.
Build reliable, well-documented software in a regulated financial environment, collaborating across engineering, security, and operations.
JPMorgan Chase & Co. · Full-time
3 yrs 7 mos
Cloud-native engineering and operations with a focus on threat detection and response across Kubernetes workloads — Azure Sentinel, Azure Security Center, and secure delivery pipelines.
Established the DevOps and platform habits I still use in MLOps: containers, observability, and automated, auditable releases.
Production AI, MLOps, and engineering work — retrieval systems, generative models, governed runtimes, and cloud security.
Milestones across principal ML roles, generative AI, software engineering, and graduate study in data science.
FirstEnergy
Current principal machine learning engineering role delivering production AI and ML systems.
Hexagon
MLOps, generative AI, LLMs, and vector databases for asset lifecycle intelligence.
Production
Language models, Pinecone, hybrid search, and RAG deployed on AWS, Azure, and GCP.
10+ yrs
Senior engineering at Charles Schwab and DevOps at JPMorgan Chase before principal ML.
M.S.
Master's degree in Data Science and Machine Learning, 2020–2021.
2019
Certificate in Data Science and Machine Learning, August 2019.
AWS · Azure · GCP
SageMaker, Azure ML, Databricks, Terraform, Docker, and Kubernetes in production.
NIST / EU AI Act
Attestation, time-bound licenses, and safe degradation for advanced AI systems.
Senior SWE
Cross-cloud IAM and software engineering in a regulated financial environment.
Industrial AI
Asset optimization, annotation, visualization, and model development at Hexagon.
AI, machine learning, and engineering skills from principal ML work at FirstEnergy, Hexagon, Charles Schwab, and JPMorgan Chase.
Open to Principal ML Engineer and AI / software engineering work — generative AI, MLOps, and production systems teams can trust.
