Description

Building (largest share of the day)

Internal platform tools and services: self-service portals/workbenches, backend APIs (Python/FastAPI), automations and CI/CD tooling

MCP/gateway integrations and AI-enabled automations and flows

Focus is always on reducing friction for teams adopting the platform

Cloud infrastructure & troubleshooting (weekly)

Writing and maintaining Terraform; provisioning and configuring platform resources across AWS and Azure

Diagnosing deployment, networking, endpoint, and configuration issues

Enough depth to reason about deployments and partner with security/networking specialists

Collaboration & enablement (about half the day)

Standups, syncs, and planning/project meetings

Design and architecture reviews; regular PR and code review

Onboarding new teams; translating ambiguous requirements into practical plans and challenging weak designs

Model deployment support (recurring)

Helping teams productionize models—hosting options, inference patterns, scaling, cost, and operational readiness across SageMaker, Bedrock, Azure ML/AI Foundry, and Kubernetes

Docs & platform improvement (ongoing)

Documentation, onboarding guides, and reference examples

Ad-hoc process and platform improvements—spotting and fixing rough edges proactively

In short: a builder-first role with a strong collaborative and enablement component—someone who moves fluidly between writing code, reviewing work, troubleshooting infrastructure, and guiding architectural decisions.

Education & Experience Recommended

Four-year or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, or any other related discipline or commensurate work experience or demonstrated competence.

Typically has 7-10 years of work experience, preferably in computer programming languages, machine learning, algorithms, statistical methods, or a related field.

Preferred Certifications

AWS Certified Machine Learning Specialty

Key Skills

Education

Bachelor's degree