Team Lead Machine Learning MLOps

ACV’s Machine Learning Team is looking for a talented, experienced Machine Learning (ML) Engineer, to lead the development of our machine learning operations. You will be leading a team of engineers to set up ML model training pipelines, deploying APIs around ML models, and monitoring ML models that power the ACV’s document processing and vehicle pricing.

What you will do:

Successful candidates will demonstrate excellent skill and maturity, be self-motivated as well as team-oriented, and have the ability to support the development and implementation of end-to-end ML-enabled software solutions to meet the needs of internal stakeholders. Those who will excel in this role will be those who listen with an ear to the overarching goal, not just the immediate concern that started the query. They will be able to show their recommendations are contextually grounded in an understanding of the practical problem, the data, and theory as well as what product and software solutions are feasible and desirable.

The core responsibilities of this role are:

• Leading a team of machine learning engineers to build, automate, and deploy ML models and pipelines in production.

• Enact ML best practices for the team to follow.

• Developing ML model pipelines from proof of concept to production.

• Documenting the work process, including pipeline set up, experiment execution, and model deployment.

• Monitoring performance of models in production.

• Collaborate with stakeholders to help meet their objectives and provide them with periodic status updates.

What you will need:

• Graduate education in a computationally intensive domain.

• 5+ years of prior relevant work or lab experience in ML projects, and 2+ years as tech lead or team management experience.

• Experience with building data pipelines (Kubeflow, sklearn pipelines, etc.).

• Experience with building and deploying REST APIs (Flask, FastAPI)

• Advanced proficiency with Python and SQL (BigQuery/MySQL).

• Extensive knowledge of ML frameworks and libraries. (Ray, Feast, ClearML, etc.)

• Experience with cloud services (AWS / GCP), Docker, Kubernetes, and CI/CD pipelines.

• Monitor models performance and microservices health status in Production.

Preferred Qualifications:

• Experience with ML model development lifecycles

• Experience with Bash scripting