AI Platform Engineer

Ethereum Technologies; Boston (MA); Sur place; USD 140 000 - 210 000; Plein temps

Résumé du poste

Ethereum Technologies is hiring for a senior platform engineer to design and operate scalable AI infrastructure. You will manage compute environments for AI workloads, integrate retrieval systems, and ensure secure, cost-aware operations across cloud and containerized platforms.

You will automate safe rollouts, build vector stores, and implement guardrails. Candidates should show strong Python skills, API integrations, and experience with cloud providers.

Qualifications

• 4+ years in platform engineering, cloud infra, or DevOps.
• Experience running AI in production with retrieval systems grounded in company data.
• Understanding AI systems differ from traditional software; know how to test, evaluate, and monitor them.
• Strong Python skills, including building integrations against APIs.
• Experience with AWS or Azure and containerized infrastructure.
• Sense of cost vs performance tradeoffs for AI models.
• Ability to expose metrics via APIs or dashboards for visibility.
• Security mindset: least-privilege access and safe handling of data.
• Self-service design principles and guardrails for AI usage.
• Comfort across infra, automation, integration, and app code.

Responsabilités

• Infrastructure and Scaling: stand up and maintain compute env for AI workloads, incl. containerized and GPU-backed resources.
• Automate safe rollouts: build pipelines for updates with rollback.
• Build retrieval systems: ground model outputs in real company data.
• Maintain vector stores and data sources for retrieval.
• Route and gate model access with governed, model-fitting routing.
• Redact sensitive data from prompts before models see them.
• Build guardrails to catch harmful or wrong AI output.
• Enable self-service while enforcing access controls.
• Monitor system health, response times, and error rates.
• Track usage and spend at model and feature levels.
• Apply cost discipline across AI spend.
• Build self-service tools and APIs for teams to ship AI features.
• Report on AI performance via dashboards and APIs.

Connaissances

Platform engineering

AI in production

AI systems judgment

Python programming

Cloud & containers

Cost/performance tradeoffs

APIs & integrations

Security mindset

Self-service thinking

Full-stack infra & code

Description du poste

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

• Infrastructure and Scaling: stand up and maintain the compute environment AI workloads run on, including containerized and GPU-backed resources, so workloads run efficiently and cost-effectively
• Automate safe rollouts: build automated pipelines for rolling out AI platform updates, with rollback built in
• Build retrieval systems: connect AI models to the organization's own data so responses are grounded in real, current information
• Maintain vector stores: connect and maintain the vector stores and data sources retrieval depends on
• Route and gate model access: give people one governed way to reach approved AI models and providers, routing each request to the model that actually fits the job
• Redact sensitive data: strip sensitive or private data out of prompts before they reach a model
• Build guardrails: put guardrails in place to catch harmful, biased, or simply wrong AI output before a user sees it
• Enable self-service without losing control: set up access and permissions so people across the organization can use approved AI capabilities on their own, while the platform still enforces where the line is
• Monitor system health: watch system health, response times, and error rates, and catch problems before people have to report them
• Track usage and spend: track usage and spend at the model and feature level so cost surprises do not happen
• Apply cost discipline: apply the same cost-management discipline to AI spend that the platform team applies everywhere else
• Build self-service tools: build self-service tools and APIs so other teams can ship AI features without needing to understand the plumbing underneath
• Report on AI performance: expose usage, spend, and performance metrics through APIs or dashboards so AI costs and performance are visible across the organization

Qualifications:

Required:

• Platform engineering experience: 4+ years of hands‑on experience in platform engineering, cloud infrastructure, or DevOps
• AI in production: experience running AI or LLM platforms in production, including retrieval systems that ground model answers in real company data
• AI systems judgment: understands that AI systems behave differently from regular software, and knows how to test, evaluate, and monitor them so problems get caught early
• Programming and automation: strong Python skills, including building integrations against APIs
• Cloud and containers: solid experience with at least one major cloud provider (AWS or Azure) and building on containerized infrastructure
• Cost and model judgment: some sense of cost and performance tradeoffs between AI models, and when a smaller, cheaper model is the right call
• Reporting and transparency: experience building or exposing metrics through APIs or dashboards for reporting and visibility beyond the immediate team
• Security mindset: builds with security as a first‑class concern: least‑privilege access, careful handling of sensitive data, sensible defaults
• Self‑service thinking: understands how to make AI capability self‑service, and where the guardrails actually need to hold
• Full‑stack comfort: equally comfortable in infrastructure, automation, integration, and application code

Preferred (Optional):

• Terraform or similar infrastructure‑as‑code experience
• Exposure to cloud cost management or FinOps practices
• Deeper experience with retrieval pipelines: chunking, embeddings, indexing, and ranking, not just calling a vector database
• Experience routing requests across multiple AI model providers based on cost, speed, or capability
• Judgment about when a simple, predictable workflow beats an open‑ended AI agent, with experience building both
• Familiarity with container orchestration for AI or compute‑heavy workloads
• Familiarity with governing tool access for AI agents, such as scoped permissions or a central tool registry, as agentic workflows become more common
• Certifications are a plus, not required. What you have actually built matters more.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.