About Meitner Energy

Meitner Energy is developing advanced nuclear energy solutions intended to deliver reliable, scalable, and carbon-free electricity for industrial and grid applications.

Our international team brings together nuclear, engineering, commercial, regulatory, and project-development experience. We are building an organization focused on disciplined engineering, responsible execution, and the deployment of nuclear energy at meaningful scale.

The Opportunity

Meitner’s AI platform is being built now, and this role owns it end to end. You will design and deliver the model-serving infrastructure, the gateway and router layer, the retrieval pipeline, and the tiered hosting architecture that allows engineers, operators, and commercial teams to use AI safely on export-controlled and otherwise sensitive technical material. You will also personally deploy and tune the serving lanes, own the pipelines and automated tests that keep the platform reliable, and run the evaluation process that determines which models reach production.

Alongside that mandate, you will act as the company’s corporate cloud and infrastructure architect, owning the public cloud footprint and the design decisions that shape where workloads and data live.

The role carries genuine engineering authority. You will set code-quality, review, testing, and release standards for the platform, serve as the primary technical interface to security, compliance, and export-control stakeholders, and lead model evaluation and selection across a fast-moving landscape of open-weight and proprietary models. The work touches Meitner’s reactor program directly, and routing sensitive material to the right place is a requirement rather than an aspiration.

This role is adecuado for someone who:

- Wants to design and build a production LLM platform rather than operate one that is already finished.
- Takes real ownership of reliability: writes tests, enforces gates, and does not ship work they are not confident in.
- Is fluent across the modern platform stack, including Python, containers, container orchestration, and high-throughput model serving, and can move quickly without cutting corners.
- Finds the intersection of high-performance inference and security-conscious regulated environments interesting rather than frustrating.
- Communicates architecture and engineering decisions clearly to executive, regulatory, and cross-site audiences without losing precision.

What You’ll Do

AI Platform Architecture and Delivery

Design and own the end-to-end architecture of Meitner’s private AI platform, including the model-serving stack, the gateway and router layer, and the retrieval pipeline. Architect and enforce the tiered hosting design that separates regulated workloads from general-purpose workloads, and ensure the resulting controls satisfy the export-control and controlled-information requirements that apply to advanced nuclear technology. Lead model selection and sequencing across candidate open-weight and proprietary models, own the evaluation harness used to make those decisions, and design GPU infrastructure sized to serve those models reliably, with a capacity plan tied to program milestones.

Model Serving and Inference

Deploy, configure, and tune high-throughput model-serving infrastructure, managing quantization strategy and GPU memory planning so that target models fit the available hardware within budget. Monitor inference latency, throughput, and utilization, identify and resolve bottlenecks across the serving stack, and support capacity planning and infrastructure sizing decisions.

Gateway, Routing, and Platform Reliability

Implement and maintain the gateway and router rules that govern access to the platform, including token accounting, prompt-injection defenses, model version pinning, and request routing between regulated and non-regulated serving tiers. Manage controlled model promotion and rollback so that platform state remains auditable and reversible,



and contribute to the platform’s security posture by supporting data-sensitivity routing and export-control requirements in the design of every change you make.

Continuous Delivery, Testing, and Engineering Standards

Own the continuous integration and delivery pipelines for platform code, and establish the automated test coverage and quality gates that enforce the platform’s engineering standards. Set and enforce the code-quality, code-review, testing-gate, and release-governance standards under which the platform is built, establish deployment and rollback practices that meet the reliability and auditability expectations of a nuclear engineering environment, and raise the engineering bar through hands-on review, pairing, and documented standards that others can follow without you in the room.

Model Evaluation

Run structured comparisons across candidate open-weight and proprietary models using the platform’s evaluation harness, covering generation and embedding models alike. Document results clearly, surface regressions, and contribute model selection recommendations supported by evidence rather than impression.

Corporate Cloud and Infrastructure

Own Meitner’s corporate cloud architecture across its public cloud providers, including infrastructure-as-code practice, cost governance, and reliability design. Make and document the decisions that determine where applications and data reside across the company’s sites, and ensure that cloud and on-premises infrastructure meets the security and data-residency requirements of a multi-jurisdiction, bilingual organization.

Compliance, Security, and Stakeholder Engagement

Serve as the primary technical interface to security, compliance, and export-control functions for AI and infrastructure decisions. Support provider selection and contribute technical design input to authorization packages, and communicate architecture decisions and trade-offs clearly to the Chief Information Officer, executive leadership, and technical teams across sites and languages.

What We’re Looking For

Required Qualifications

- Bachelor’s degree in computer science, engineering, or a related technical discipline, or equivalent professional experience.
- 3 to 10 years of professional experience in MLOps, platform engineering, infrastructure engineering, or solutions architecture, with a focus on machine learning or LLM systems.
- Hands-on production experience deploying and operating a high-throughput model-serving framework for large language models, including quantization trade-offs and the latency, throughput, and cost trade-offs behind your design decisions.
- Proficiency in Python, containers, and production container orchestration, with the ability to debug a serving outage, trace a gateway misconfiguration, and read infrastructure-as-code confidently.
- Experience building and maintaining continuous integration and delivery pipelines with automated testing and release gates.
- Working knowledge of inference performance tuning, GPU memory constraints, and gateway or routing frameworks for model access.
- Strong architecture proficiency across major public cloud platforms, with demonstrated declarative infrastructure-as-code practice.
- Demonstrated experience designing for or operating within export-controlled, controlled unclassified information, or similarly regulated data environments, including reasoning independently about data-sensitivity routing and security boundaries.
- A track record of setting and holding engineering standards on a technical team, covering code review, testing, and release governance.
- Excellent written and verbal communication for executive, regulatory,



and cross-site audiences.
- Ability to work on-site in Dallas and travel internationally periodically.

Preferred Qualifications

- Working proficiency in Spanish. Spanish is strongly preferred because the position requires frequent technical collaboration with colleagues in Argentina.
- Experience provisioning GPU infrastructure and planning capacity for inference workloads.
- Exposure to retrieval pipeline implementation and embedding model deployment.
- Familiarity with export-control or comparable information-handling frameworks in nuclear, defense, aerospace, or another regulated industrial setting.
- Experience collaborating with technical teams in Argentina or Latin America.
- Relevant professional certifications in cloud architecture, security, or platform engineering.

The Candidate We Are Seeking The strongest candidate will be a platform engineer or architect who has shipped production LLM systems and remained close to the implementation. This may be an excellent next step for a senior platform engineer, principal infrastructure engineer, MLOps engineer, or solutions architect who is ready to hold full architectural authority over a platform and to personally build and defend it. Candidates should be prepared to discuss systems they have personally designed, deployed, tuned, broken, and repaired.

Why Join Meitner Energy?

Consequential Work. Design and build the platform that lets engineers and operators apply AI to the delivery of reliable, scalable, carbon-free energy.

Direct Ownership. Architect, build, tune, and defend the platform end to end rather than inheriting someone else’s design. Meitner is small enough that your decisions visibly shape the company.

International Scope. Work with professionals across the United States, Argentina, and the United Kingdom.

Strong Benefits. Meitner offers comprehensive health insurance, a 401(k) retirement plan, and participation in the company’s employee stock option program, subject to plan terms and eligibility. The final offer will reflect the candidate’s depth and relevant skills, including platform depth, serving depth, cloud architecture experience, delivery and testing experience, Spanish proficiency, and regulated-industry background.

High-Quality Workplace. Work from a modern Dallas office designed to support collaboration, productivity, and employee well-being, including an on-site fitness facility.

To Apply

Email your resume and a written response of no more than one page to [email protected]. Use this exact subject line: AI Platform Engineer / your full name / hands-on. Applications without it will not be reviewed.

Write the response yourself, in your own words, for this posting specifically. Answer directly with specific examples; generic statements of engineering philosophy or best practices are not considered responsive, and responses that appear mass-produced or machine-generated are declined without review. Your one page should address the following:

- Quote the single sentence from this posting that best describes how this role differs from your current position, and explain why that difference appeals to you.
- Describe the most complex AI or LLM platform system you have designed, built, or operated in the past three years. What was the stack, what were the hardest trade-offs, what broke, and how did you fix it?
- Walk us through how you would fit a large open-weight model onto a fixed GPU memory budget. What trade-offs would you consider, and how would you verify the result?
- Describe how you have approached data-sensitivity routing or access control in a regulated or security-sensitive environment. What did you build, and how did you verify it held?
- Explain why this role is the right next step in your career, and identify one aspect of the position that may be more demanding than your current role.

Please do not include confidential, proprietary, export-controlled, or otherwise restricted information belonging to a current or former employer.

📌 AI Platform Engineer / Solutions Architect (Provincia de Buenos Aires)
🏢 Meitner Energy
📍 Provincia de Buenos Aires