Senior AI/ML Engineer

Job Title: Senior AI/ML Engineer
Location: Remote

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. The impact of our work empowers the doers of good: non-profit, public-sector, and grantmaking organizations whose goals are to help those who need it most.

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. The ideal candidate will have reverence for truth, humility, willingness to learn, and a passion for social good.

What You ll Do

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.

Requirements:

5+ years of experience in ML Engineering, Data Engineering, or related roles (including 2+ years in a senior or lead capacity).

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.

Demonstrated ability to operationalize models.

Strong communication skills, with the ability to influence technical and non-technical stakeholders.

Nice to Have

Experience deploying and optimizing LLMs or generative AI models in production.

Familiarity with governance, compliance, and InfoSec practices in regulated data GPT environments.