Instructure Hungary Ltd

Budapest, Balatoni út 2/a
40% irodai munkavégzés, 60% távmunka

1.8M – 2.2M HUF alkalmazotti havi bruttó

Elvárások

• AWS architecture
• production infrastructure
• system design
• distributed systems
• CI/CD and deployment patterns
• containerization and orchestration
• reliability and observability
• infrastructure-as-code
• production ML or AI service deployment
• Angol (C1)

A mi követelményeink

Eredeti szöveg. Fordítás megjelenítése

What We’re Looking For

• Strong experience designing and operating production systems on AWS.
• Deep understanding of distributed systems, cloud architecture, scalability, reliability, and service design.
• Hands-on experience with infrastructure-as-code, CI/CD, Docker, Kubernetes, and production deployment workflows.
• Experience building or supporting production ML, AI, data, or high-scale backend systems.
• Strong system design skills, including the ability to reason about tradeoffs, failure modes, data flow, service boundaries, and operational complexity.
• Ability to communicate clearly across data science, ML engineering, backend engineering, platform engineering, product, and leadership stakeholders.

Nice to Have

• Experience with SageMaker, Bedrock, ECS, EKS, Lambda, S3, RDS, OpenSearch, Aurora, EventBridge, Step Functions, or related AWS services.
• Experience with model serving, batch inference, embedding pipelines, vector databases, RAG systems, or LLM-backed applications.
• Experience building ML platform capabilities such as model registries, experiment tracking, evaluation pipelines, inference services, or model monitoring.
• Experience supporting both real-time and batch AI/ML workloads.
• Experience with workflow orchestration, data pipelines, and production evaluation frameworks.
• Experience defining production-readiness standards for AI systems, including evaluation gates, model/version drift, data quality checks, and cost monitoring.

You Might Be a Great Fit If

• You enjoy designing systems from first principles and can explain architecture tradeoffs clearly.
• You have built infrastructure that other engineers depend on.
• You are comfortable operating at both architecture and implementation levels.
• You understand that ML systems involve data, models, evaluation, versioning, latency, uncertainty, and operational risk.
• You can take an ambiguous AI/ML need and turn it into a practical technical architecture.
• You care deeply about scale, reliability, modularity, maintainability, and developer experience.

What Success Looks Like

• You understand the AI/ML team’s workflows, infrastructure gaps, and production bottlenecks.
• You define reusable architecture patterns that help AI/ML services move from prototype to production.
• You establish reliable deployment, monitoring, rollback, and operational standards for AI/ML systems.
• You reduce friction for data scientists and applied AI engineers by creating clear production pathways.
• You help teams build AI/ML systems that are scalable, secure, observable, and maintainable.
• Your work becomes part of the foundation for scaling AI capabilities across Instructure products.

Onsite Collaboration Requirement:

This role requires working onsite on Tuesday and Wednesday, with Thursday strongly encouraged as part of our company’s in-person collaboration model.

Pozíció / projekt rövid leírása

Eredeti szöveg. Fordítás megjelenítése

Instructure is building foundational AI and machine learning capabilities that will power the next generation of learning experiences across our product ecosystem. We are looking for a Senior AI/ML Platform Architect / Engineer to design and build the AWS-native infrastructure layer that enables data scientists, ML engineers, and applied AI teams to move from prototype to production safely, reliably, and at scale.

This is not a research role and not a traditional DevOps role. It is a hands-on systems architecture role for someone who understands cloud infrastructure, production reliability, and the unique needs of AI/ML workloads.

You will partner closely with data science, applied AI, backend engineering, platform engineering, and product teams to translate emerging AI/ML needs into scalable, modular, production-grade systems.

Napi feladatok

Eredeti szöveg. Fordítás megjelenítése

1. Design and build the AWS-native production infrastructure for AI/ML services, including deployment, observability, reliability, and operational readiness.
2. Partner with ML, data science, and applied AI teams to understand their workflows and translate prototype needs into scalable architecture.
3. Create reusable infrastructure patterns, service templates, CI/CD pipelines, and deployment workflows for AI/ML workloads.
4. Architect systems for model serving, batch inference, retrieval pipelines, evaluation workflows, and AI service deployment.
5. Define production standards for monitoring, alerting, rollback, logging, versioning, and reliability of AI/ML systems.
6. Collaborate with platform and backend engineering teams to ensure AI/ML infrastructure aligns with broader company architecture and security standards.

Specifikációk

• Online állásinterjú
• Toborzás nyelvei: magyar&angol
• Azonnali kezdés
• Távmunka heti 3 nap
• Rugalmas munkaidő

A toborzási folyamat lépései

1. Recruiter screen
2. Technical interview 1
3. Technical interview 2
4. Manager round

Biztosított eszközök

• Apple
• Monitorok: Egy

Irodán belüli juttatások

• Ingyenes kávé
• Kerékpártároló
• Szórakozási zóna
• Zuhanyzó
• Ingyenes snack
• Ingyenes italok
• Ingyenes parkolás
• Belső képzések
• Belső hack napok
• Modern iroda
• Startup hangulat
• Nincs dress code
• Ingyenes reggeli
• Hack-weeks
• Employee assistance program

Extrák

• Nemzetközi projektek
• Kis létszámú csapat
• Lapos szervezet

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