Description

We are looking for a Senior Backend Engineer to join our ML Platform Engineering team!

Our team is responsible for enabling and optimizing machine learning operations at scale - from model deployment to production monitoring. We work with cutting-edge serverless technologies to ensure our ML models perform efficiently and reliably in production environments, directly impacting our product's success and business outcomes. You'll be joining a team of dedicated professionals who bridge the gap between data science innovation and production excellence, working to create robust, scalable solutions that power our ML infrastructure.

Who are we?

Voyantis was founded in 2020 on the premise that market fundamentals are shifting companies worldwide from growth-at-all-costs strategies to efficient and responsible growth practices, with a focus on improving Unit Economics. With a bold mission to leverage AI to reimagine the whole Growth process, to streamline this transition and ensure its sustainability, Voyantis eliminates the guesswork from customer value creation, empowering leaders with actionable strategies and tactics to acquire, nurture and retain the high-value customers their businesses really need, with the actions and the timing that would be most impactful to achieve their goals.

Leading companies like Miro, Rappi and Moneylion rely on Voyantis to effectively apply these predictions. They use Voyantis to drive high-value customer acquisition on platforms like Google and Meta, optimize incentives through Salesforce and Braze, and perfectly time upsells, resulting in a 20%-40% ROI uplift.

Voyantis is well-backed by top VCs such as Target Global and SquarePeg. The company has tripled in size annually over the past two years and now boasts a team of 70 with offices in California, New York and Tel Aviv.

Responsibilities

• Design and architect scalable ML infrastructure and pipelines using serverless architecture, focusing on long-term maintainability and performance
• Lead technical initiatives and drive architectural decisions for ML infrastructure improvements
• Collaborate with data scientists and ML engineers to optimize model deployment processes and improve operational efficiency
• Design and implement robust monitoring systems for ML models in production, ensuring reliability, optimizing performance, and leading complex troubleshooting efforts
• Develop and maintain sophisticated data transformation pipelines using modern tools like dbt and Snowflake
• Establish and enforce CI/CD best practices for ML models and supporting infrastructure
• Drive and evangelize best practices in software engineering and ML Infra across the organization

Requirements

• 5+ years of hands-on experience designing and developing scalable backend infrastructure, with strong proficiency in Python.
• Extensive experience with cloud services, particularly serverless architecture
• Strong SQL proficiency with experience in data modeling and query optimization
• Proven experience with data engineering concepts and ETL processes
• Strong understanding of software engineering best practices, including version control, testing, and CI/CD
• Demonstrated ability to lead technical initiatives and drive architectural decisions
• Excellence in problem-solving with strong attention to detail and system design capabilities

The following will be considered a plus:

• Deep understanding of data warehouse design patterns (Star Schema, OBT, Slowly Changing Dimensions)
• Experience with DBT for data transformation and data modeling
• Experience with infrastructure as code (Pulumi or equivalent)
• University degree in Computer Science, Engineering, or related field
• Previous experience in a similar role supporting ML teams