Senior Infrastructure Engineer, AI & ML

Bertelsmann SE & Co. KGaA

. Design and own core components of the AWS environment used across data science products .

Posted 11/9/2026full-timeRemote • New York • MaliSenior💰 $185,000 - $210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing AWS cloud environments for data science applications, with a focus on building production systems and ensuring security and compliance. Proficient in developing CI/CD pipelines and collaborating with data scientists to enhance infrastructure for machine learning workloads.

Highest-signal resume keywords

AWS Cloud ExperienceProduction Systems ManagementCI/CD Pipeline DevelopmentInfrastructure Design for ML WorkloadsStrong Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Infrastructure DesignProduction SystemsCI/CD PipelinesCloud SecurityCost Management for ML SystemsError Analysis of ML SystemsSLOs and SLAs DefinitionAI Agent UtilizationData-Intensive WorkloadsModel Serving

Soft Skills

Strong CommunicationCollaboration with Data ScientistsStakeholder Management

Industry Keywords

Cloud EnvironmentsData Science ProductsMachine LearningModel TrainingBatch Scoring

Tech Stack

Tools & technologies

AWSCloud

About the role

Key responsibilities & impact

Design and own core components of the AWS environment used across data science products
Set standards for how people and AI agents change infrastructure through reviews, tests, and guardrails
Build self-service production paths for scientists, including CI/CD pipelines, promotion gates, and automated triggers
Help maintain the security, compliance, and reliability of cloud environments
Shape the platform roadmap by identifying infrastructure work that unblocks scientists
Keep stakeholders informed as platform work progresses
Work closely with AI to evolve and run the cloud platform

Requirements

What you’ll need

5+ years building and running production systems
Track record of designing infrastructure components or tooling adopted by other engineers or teams
Hands-on cloud experience, AWS preferred
Ability to use AI agents to build and operate systems, with demonstrable shipped work
Strong communication skills and ability to work closely with data scientists and engineers
Experience running infrastructure for ML or data-intensive workloads in production, such as model training, batch scoring, or model serving
Experience defining SLOs or SLAs and managing cloud cost for ML production systems preferred
Experience with evaluation or error analysis of ML or LLM systems in production preferred
Resume and cover letter required for consideration

Benefits

Comp & perks

Annual profit award or bonus, subject to Company results
Medical/Prescription drug insurance
Dental insurance
Vision insurance
Health Care/Dependent Care Flexible Spending Account
Health Savings Account
Pre-Tax and Roth 401(k)
Short- and Long-Term Disability Insurance
Life/AD&D Insurance
Commuter Benefits
Student Loan Repayment Program
Educational Assistance
Generous paid time off