Mid Level AI Infrastructure Engineer Jobs
Mid level ai infrastructure engineer jobs go to engineers ready to own distributed systems end to end, make architecture decisions with limited oversight, and mentor junior teammates through complex deployments. Openings run 27% remote or hybrid across Technology & Software, Investment & Asset Management, and Banking & Financial Services, with employers like Anrok, Figma, and Deloitte hiring at this level now.
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Showing 5 of 31+ Mid Level AI Infrastructure Engineer jobs
Job description
Role: AI Infra Engineer
Location: Remote (US)
Experience:
5+ years
Skills:
- Data Engineering
- Python
- PySpark
- Machine Learning
- API
- Streamlit
Responsibilities:
- 5+ years in software/ML infrastructure engineering
- Build full-stack internal GenAI tools (Python, FastAPI, Streamlit) that let non-engineering teams run LLM analysis over sensitive data, including scalable PII-masking pipelines.
- Design and maintain MCP servers/tools enabling teams to run workflows directly from AI chat clients, with strict identity and auth checks.
- Own and evolve labeling platform infrastructure (FastAPI, Celery, Redis, Labelbox), including large-scale data migrations, on-call support, and data-loss recovery.
- Migrate observability stacks from legacy tools to OpenTelemetry, building dashboards and ing as code.
- Build and maintain data/ML pipelines (Airflow, Spark, Hive, Trino) supporting multiple business units.
- Partner with product and engineering leads to turn ambiguous problems into shipped features with measurable efficiency gains.
Job description
Role: AI Infra Engineer
Location: Remote (US)
Experience:
5+ years
Skills:
- Data Engineering
- Python
- PySpark
- Machine Learning
- API
- Streamlit
Responsibilities:
- 5+ years in software/ML infrastructure engineering
- Build full-stack internal GenAI tools (Python, FastAPI, Streamlit) that let non-engineering teams run LLM analysis over sensitive data, including scalable PII-masking pipelines.
- Design and maintain MCP servers/tools enabling teams to run workflows directly from AI chat clients, with strict identity and auth checks.
- Own and evolve labeling platform infrastructure (FastAPI, Celery, Redis, Labelbox), including large-scale data migrations, on-call support, and data-loss recovery.
- Migrate observability stacks from legacy tools to OpenTelemetry, building dashboards and ing as code.
- Build and maintain data/ML pipelines (Airflow, Spark, Hive, Trino) supporting multiple business units.
- Partner with product and engineering leads to turn ambiguous problems into shipped features with measurable efficiency gains.
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Find JobsMid Level AI Infrastructure Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software16
- Investment & Asset Management5
- Banking & Financial Services4
- Consulting & Professional Services4
- Fintech3
Mid Level AI Infrastructure Engineer Jobs: Frequently Asked Questions
How do I get a mid level ai infrastructure engineer job?
Position yourself around ownership, not just contribution. Highlight projects where you designed or improved a system independently, made tradeoffs you can explain, and delivered measurable results. Emphasize hands-on experience with ML pipelines, GPU cluster management, container orchestration, or model serving infrastructure. Tailor your application to show you can operate without close supervision, not just execute tasks assigned by others.
Which companies hire mid level ai infrastructure engineers?
Companies hiring mid level ai infrastructure engineers right now include Anrok, Figma, and Deloitte, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a mix of AI-native startups scaling their compute infrastructure and large enterprises building out dedicated machine learning platform teams.
Are there remote mid level ai infrastructure engineer jobs?
Yes, and remote availability at this level is strong. About 27% of mid level ai infrastructure engineer openings are remote or hybrid as of November 2026, reflecting how infrastructure work translates well to distributed teams. On-site roles do exist, particularly at companies running sensitive on-premise GPU clusters or working in regulated industries.
How do I move up to a mid level ai infrastructure engineer role?
Moving from entry level to mid level means shifting from task execution to project ownership. Build depth in one or two core areas, such as distributed training infrastructure, model deployment pipelines, or Kubernetes-based ML platforms, then find opportunities to lead a deliverable from design to production. Demonstrating that you caught a failure, proposed a solution, and saw it through carries more weight than breadth alone.
Which industries hire the most mid level ai infrastructure engineers?
Mid Level ai infrastructure engineer roles concentrate in Technology & Software, Investment & Asset Management, and Banking & Financial Services, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they are actively scaling AI workloads and need engineers who can build and maintain the underlying compute, storage, and orchestration systems without requiring senior-level oversight on every decision.