Senior AI-ML Engineer, Bonterra

• Strong programming skills in Python; proficiency in SQL
• Proven experience with Snowflake and dbt for production data pipelines
• Expertise with AWS (including Bedrock, and SageMaker) and modern cloud-native ML infrastructure
• Familiarity with containerization (Docker), APIs, and orchestration tools (dbt Cloud and AWS-native schedulers)
• Deep understanding of CI/CD pipelines, testing frameworks, and production monitoring
• Strong communication skills, with the ability to influence technical and non-technical stakeholders
• Required Minimum Qualifications
• 5+ years of experience in ML Engineering, Data Engineering, or related roles (including 2+ years in a senior or lead capacity)
• Experience deploying and optimizing LLMs or generative AI models in production

Desirable

• Bonterra’s Data Science and Analytics team embeds directly within Product teams to build out AI features, owning product analytics, modeling, and production deployment. Our Senior AI/ML Engineers set the standard for how Data Science models are deployed, scaled, and maintained in production.
• This role combines technical depth with leadership: you’ll design and optimize the infrastructure that powers AI/ML, mentor others on best practices, and ensure that our data science work is production-ready, secure, and reliable. You will be a key partner to Product and Engineering in bringing innovative models into production at scale
• This role requires both the scientific mindset of engaging deeply with problems and experimentation and the engineering mindset of building solutions that are reliable, repeatable, and production-ready
• Lead the design, build, and maintenance of scalable, reliable, and automated data pipelines for ML model training and inference
• Drive adoption of MLOps/DataOps best practices: CI/CD, automated testing, monitoring, observability, and rollback strategies
• Own the management and optimization of our cloud infrastructure (AWS, Bedrock, Snowflake, dbt) to support large-scale ML workflows
• Ensure data quality, security, and governance throughout the ML lifecycle
• Translate Data Science prototypes into production-ready services, APIs, and pipelines
• Collaborate cross-functionally (Data Science, Engineering, Product, Analytics) to deliver ML solutions that are trusted and accessible
• Proactively monitor production systems, troubleshoot issues, and ensure high uptime and reliability
• Mentor and guide data scientists on engineering best practices
• Influence long-term technical direction, including architecture choices for ML platforms