Why This Job is Featured on The SaaS Jobs
This AI Platform Engineer role sits in a core layer of modern SaaS architecture: the shared data and ML platform that multiple product teams depend on. The scope spans warehouse design, event streaming, and managed ML infrastructure, indicating a company investing in operationalising AI capabilities rather than treating them as isolated experiments. In SaaS terms, it is platform work that shapes reliability, governance, and speed of iteration across the product surface.
For a long-term SaaS career, the value is in building durable primitives that scale with usage and organisational complexity. Ownership of data contracts, observability, and privacy controls maps directly to the realities of subscription software, where retention, trust, and predictable performance are product features. Experience bridging AI engineers, data science, and product engineering is also highly transferable as more SaaS teams adopt ML pipelines and require repeatable deployment patterns.
This position fits engineers who prefer foundational problems over feature delivery and who enjoy turning ambiguous data needs into stable, automated systems. It will suit someone comfortable being a cross-team partner, balancing governance with enablement, and thinking in operational terms such as lineage, freshness, and access boundaries. It also aligns with professionals who like infrastructure-as-code discipline and measurable platform outcomes.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As an AI Platform Engineer on AI Mining, you'll own the data infrastructure layer that makes skill mining possible. That means BigQuery datasets and schemas, cloud storage, service accounts and IAM, Vertex AI pipeline infrastructure, Dataform integration, and the event streaming resources that connect mining outputs to the rest of the platform. It also means building the automated pipelines that move data into place — scheduled jobs, data copy pipelines, backfill orchestration — so the right data is where it needs to be when the mining team needs it.
You'll work at the intersection of data engineering and infrastructure — your work is what AI engineers, data scientists, and product engineers stand on when they ship.
What you'll do
- Get oriented on the data infrastructure landscape: BigQuery datasets and schemas, cloud storage, IAM surface, Vertex AI pipeline setup, Dataform integration, and event streaming topics.
- Be paired with a peer and your manager — you'll have context and support before you're expected to own anything independently.
- You'll be exposed to the open source technologies we depend on. We employ many open source tools to get the job done, and we love to contribute back to those communities.
- Ship your first infrastructure changes: a new dataset, a storage resource, an IAM policy, or a schema update that unblocks the mining team.
- Own core data infrastructure: BigQuery datasets, preprocessing storage buckets, service accounts, and access policies across environments.
- Partner with AI and data science engineers to translate new pipeline and data needs into provisioned, governed infrastructure.
- Contribute to data quality checks, freshness monitoring, and anomaly detection for mining inputs and outputs.
- Strengthen the team's infrastructure-as-code practices (Terraform, CI/CD).
- Drive expansion of the data platform as the team grows into new channels, customer segments, and verticals.
- Own the data governance posture: retention, anonymization, opt-out handling, and access separation for privacy-sensitive content.
- Be a trusted partner for AI, backend, product, and analytics teams — they rely on you for reliable data contracts and predictable infrastructure.
Skills you'll bring
- You have strong experience in data engineering, data platform, or infrastructure work with a data focus.
- You're deeply comfortable with BigQuery or a comparable cloud data warehouse — schema design, dataset management, and access governance feel natural.
- You have hands-on experience with infrastructure-as-code; you've provisioned cloud resources and know what good looks like.
- You think in data contracts — freshness, lineage, quality, and downstream dependencies.
- You're comfortable with privacy-sensitive data: you understand why retention policies, anonymization, and access separation matter and how to implement them correctly.
- You work well across ambiguity — teams come to you with a data need, and you can turn it into a concrete, durable infrastructure plan.
- You measure and monitor everything. You're not done when it's provisioned; you're done when it's observable.
- You enjoy working on a distributed team and value async communication as much as in-person collaboration.
