Arpeely is a Data-Science startup, leveraging data analysis, ML, engineering, multi-disciplinary thinking to gain a market edge and exploit hidden opportunities in real-time advertising. Processing over 350k requests per second and serving over 20B sub-second predictions daily, we build and operate Machine Learning algorithms running on the world’s largest Real-time-bidding (RTB) Ad-Exchanges. Arpeely is a Google AdX vendor and serves clients spanning from startups to Fortune-50 companies.

About The Roll:

We are looking for a passionate Senior Infra Engineer to join our all-star team.

This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced results, marketing and data-oriented environment.

If you are experienced but still hungry to learn and impact - we’d love to have you on our team!

On a typical day, you will:

• Design, scale, and operate high-throughput, low-latency infrastructure supporting RTB bidders and ML prediction services at extreme scale (2Million+ QPS).
• Own production GCP environments end-to-end, including deployment, monitoring, 99.999 uptime, incident response, and post-mortems.
• Build and maintain infrastructure for massive data ingestion (up to 1TB per hour), continuous ML training pipelines, and real-time prediction systems.
• Develop and evolve CI/CD pipelines, DevOps automation, and infrastructure-as-code practices.
• Work closely with engineering and data science teams, taking full ownership from design through production and ongoing operations.
• Continuously evaluate and introduce improvements to our infrastructure stack, tooling, and operational practices.

Our core stack includes GCP, Kubernetes, Prometheus, Grafana, Python, Go, BigQuery, Redis, Prefect, Aerospike and MySQL. We are looking for someone experienced, independent, and opinionated about production systems, but still curious and eager to improve how things are built and operated.

Requirements:

• At least 4 years of experience in a DevOps, Infrastructure, or MLOps role, or AIOps preferably in a startup or high-scale environment.
• Strong understanding of systems, infrastructure, and how modern distributed applications are built and scaled.
• Experience with cloud infrastructure, preferably GCP.
• Familiarity with Kubernetes-based production systems and observability tools.
• Experience working with Redis and relational or NoSQL databases or data warehouses.
• Ability to think in terms of system architecture and long-term scalability, not just short-term fixes.
• A strong sense of ownership, urgency, and responsibility for production systems.

Advantages:

• Proven experience running ML systems in production.
• Experience with high-throughput data pipelines and real-time systems
• Background in infrastructure supporting data science or ML teams.

Why Join

This is not a role for maintaining a static system. You will work on infrastructure that directly impacts real-time decision-making at a massive scale. The feedback loop is immediate, the challenges are real, and the systems you build will be pushed to their limits daily. You will join a multidisciplinary, high-caliber team in a fast-paced, results-driven environment, with real ownership and influence over core production systems.