In this role, youll work on backend services that process and serve billions of data points, power ML-driven insights, and operate at high scale and high reliability. If youre motivated by hard technical challenges, modern infrastructure, and collaborating with strong engineers, this role is built for you.
RESPONSIBILITIES:
Design, build, and evolve large-scale, distributed backend systems that power our data and AI products.
Own services end-to-end – from architecture and design to production, monitoring, and continuous improvement.
Drive performance, scalability, and reliability, using data and metrics to guide decisions.
Build infrastructure and data pipelines that support machine-learning, analytics, and real-time use cases.
Partner closely with Product, Data Science, and Analytics to turn complex data into impactful product features.
Raise the engineering bar through code reviews, design discussions, and technical mentorship.
REQUIREMENTS:
7+ years of backend engineering experience in high-scale B2B SaaS or data-driven products.
Proven track record building and maintaining production-grade distributed systems.
Strong proficiency in one or more of: Python, Java, Go, Node.js, .NET – with a bias toward Python.
Solid engineering fundamentals: system design, data structures, APIs, testing, and observability.
Hands-on experience with data-intensive systems and complex data pipelines.
BSc in Computer Science or equivalent practical experience.
AI Fluency:
A genuine early adopter of emerging AI technologies – continuously explores new tools and paradigms, integrates them into practice ahead of the curve, and actively drives that culture within their team.
Applies AI tooling to elevate engineering quality: accelerating development cycles, improving code quality, automating repetitive work, and pushing the boundaries of what the team ships.
Raises the bar around them: inspires and challenges peers to think differently about how AI can change the way they build.
OTHER REQUIREMENTS:
Deep experience with Python and MongoDB.
Experience with Google Cloud data technologies (BigQuery, Bigtable).
Hands-on with Docker, Kubernetes, and Infrastructure as Code.
Exposure to AI/ML-adjacent systems: data pipelines, feature stores, model serving, or analytics platforms.
Experience integrating or building on top of LLM APIs or AI services.
Familiarity with frontend frameworks or full-stack collaboration.
