MLOps AI Engineer

TeamViewer

MLOps AI Engineer building TeamViewer’s production data and AI platform for digital workplace automation. Operating model infrastructure, evaluation, observability, governance, and delivery pipelines in Austin.

Posted 8/7/2026full-timeAustin • Texas • 🇺🇸 United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in building and operating data and AI platforms, with a strong focus on MLOps practices, data governance, and production-grade data pipelines. Proficient in Python and SQL, with a solid understanding of CI/CD processes and cloud platforms like Azure or GCP.

Highest-signal resume keywords

Python ExpertiseSQL KnowledgeMLOps PracticesCI/CD ImplementationData Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentModel Workload ManagementObservability for AI SystemsRetrieval OptimizationAI-Specific Quality MonitoringDataset ManagementRegression TrackingDeployment StrategiesCost GovernanceEncryption

Soft Skills

Problem-Solving SkillsClear CommunicationIndependent Work

Tools & Technologies

AzureGCPCI/CD ToolsAI Coding AgentsAgentic Development Environments

Industry Keywords

Data GovernanceGDPR RequirementsAI-Powered SystemsProduction InfrastructureModel Versioning

Tech Stack

Tools & technologies

AzureCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Build and operate the data and AI platform behind TeamViewer’s agentic products, including ingestion, transformation, embeddings, indexing, retrieval, warehouses, lakes, and vector stores
Own production infrastructure for model workloads, including deployment, versioning, routing, caching, rate limiting, cost governance, and provider management
Build observability for AI systems, including agent traces, tool calls, quality signals, latency, token cost, failure-mode analysis, and AI-specific alerting
Implement CI/CD for AI systems, including testing, deployment, monitoring, and safe rollback of prompts, tool definitions, retrieval configurations, evaluation suites, and model versions
Run evaluation infrastructure for AI engineers, including dataset management, harness execution, regression tracking, and release reporting
Define and enforce data quality, governance, security, privacy, access control, encryption, and sensitive-data handling
Design experimentation platforms to test AI and retrieval changes safely against real traffic
Collaborate with AI engineers, software engineers, product, security, and platform teams to create reliable production systems

Requirements

What you’ll need

8+ years of industry experience
Strong Python expertise
Solid SQL knowledge
Sound software engineering fundamentals
Proven track record building production-grade data pipelines and platform services
Hands-on experience operating model-based applications in production, including retrieval pipelines, embeddings, vector databases, retrieval optimization, deployment, and observability
Strong understanding of MLOps and LLMOps practices, including model and prompt versioning, evaluation frameworks, tracing, regression tracking, and AI-specific quality monitoring
Ability to optimize AI workloads across providers and architectures by balancing cost, latency, reliability, and quality through caching and deployment strategies
Experience with CI/CD, automated testing, and major cloud platforms
Azure or GCP preferred
Understanding of data governance, security, privacy, and GDPR requirements within AI-powered systems and workflows
Regular use of AI coding agents, with critical review and accountability for software correctness, security, and maintainability
Practical experience with agentic development environments and extension models, including custom tools, MCP servers, repository-level instruction files, and sub-agents
Deep understanding of AI failure modes, including hallucinations, context degradation, prompt injection, non-determinism, and silent regressions, with effective mitigation approaches
Strong problem-solving skills and ability to work independently
Experience debugging complex systems
Pragmatic approach to engineering trade-offs
Clear communication
Fluency in English
Must verify identity and eligibility to work in the United States
TeamViewer cannot provide sponsorship for employment or work authorization now or in the future

Benefits

Comp & perks

Competitive compensation and bonuses
Flexible PTO and paid holidays
401(k) with employer matching
Comprehensive Health insurance package including 100% employer-paid medical coverage
Up to 12 weeks of Parental Leave
Basic Life Insurance
Short-Term & Long-Term Disability, 100% employer-paid
Quarterly teambuilding events
Leadership luncheons
Companywide “All Hands” meetings
Open door policy
Business casual dress code
Inclusive workplace culture
Support for accommodations or adjustments during the application or hiring process, including interview or onboarding support
Opportunities for employees to grow personally and professionally