AI Platform Engineer

Quantum Technologies. LLC; Boston (MA); On-site; USD 140,000 - 190,000; Full time

Job summary

Quantum Technologies. LLC is seeking a platform engineer to stand up and scale AI compute environments, including GPU-backed resources and containerized workloads. The role focuses on automated rollouts, grounded retrieval systems, and secure access controls, with emphasis on guardrails and governance.

You will monitor health, usage, and costs while enabling self-service across teams. You will work across infrastructure, automation, integration, and application code, applying cost-aware

Qualifications

• 4+ years platform engineering, cloud infra or DevOps.
• Experience running AI/LLM platforms in production, including retrieval systems grounded in company data.
• AI systems require testing, evaluation, and monitoring to catch issues early.
• Strong Python skills and API integration experience.
• Cloud and container experience with AWS or Azure.
• Knowledge of cost versus performance tradeoffs in AI models.
• Experience exposing metrics via APIs or dashboards for visibility beyond the team.
• Security-focused development with least-privilege access and data handling best practices.
• Self-service mindset with governance to enforce guardrails.
• Comfort across infrastructure, automation, integration, and application code.

Responsibilities

• Stand up and maintain the compute environment for AI workloads, including containerized and GPU-backed resources.
• Automate safe rollouts with rollback built in to update AI platforms.
• Build retrieval systems to ground model responses in real company data.
• Maintain vector stores and data sources relied on by retrieval.
• Route and gate model access to appropriate models and providers.
• Redact sensitive data from prompts before model execution.
• Implement guardrails to catch harmful or incorrect AI output.
• Enable self-service access with governance and permissions.
• Monitor system health, latency, and error rates; preempt problems.
• Track usage and spend at model and feature levels to avoid cost surprises.
• Apply cost discipline across AI spend.
• Develop self-service tools and APIs for other teams to ship AI features.
• Report AI performance metrics via dashboards and APIs.

Skills

Platform engineering

AI in production

AI systems judgment

Python programming

Cloud & containers

Cost and model judgment

APIs & dashboards

Security mindset

Self-service thinking

Full-stack

Tools

Terraform

FinOps

Retrieval pipelines

Vector databases

Container orchestration

Job description

• 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.