PitchBook is looking to hire an Engineering Manager, Machine Learning (ML) Operations to lead and manage their MLOps team. This team is responsible for providing tools and optimizing the Machine Learning Development Life Cycle (MLDLC) to support various AI projects, including Generative AI, LLMs, NLP, Classification, and Regression. The goal is to drive AI innovations across the organization by enabling ML teams and practitioners.
Requirements
- 6+ years of experience in hands-on development of Machine Learning algorithms
- 6+ years of experience in hands-on deployment of Machine Learning services
- 6+ years of experience supporting the entire MLDLC, including post-deployment operations such as monitoring and maintenance
- 6+ years of experience with Amazon Web Services (AWS) and/or Google Cloud Platform (GCP)
- Experience with at least 70%: PyTorch, Tensorflow, LangChain, scikit-learn, Redis, Elasticsearch, Amazon SageMaker, Google Vertex AI, Weights & Biases, FastAPI, Prometheus, Grafana, Apache Kafka, Apache Airflow, MLflow, and KubeFlow
- Experience in cloud-native delivery with a deep practical understanding of containerization technologies such as Kubernetes and Docker, and the ability to manage these across different regions
- Proficiency in GitOps and creation/management of CI/CD pipelines
Responsibilities
- Lead the MLOps team direction and execution (operations, processes, practices, and standards), working closely with engineering leadership and product management to craft roadmaps, define KPIs, and achieve success criteria
- Ensure effective communication and coordination across geographically dispersed teams.
- Oversee the enablement of scalable solutions that meet high standards of reliability and efficiency
- Champion the adoption and integration of ML best practices at PitchBook, fostering a culture of innovation and experimentation to drive the development of high-quality AI products
- Serve as a force multiplier by removing roadblocks, implementing process improvements, providing frequent and actionable feedback to team members, and building practices for ideation and innovation
- Bridge the gap between business/product needs and execution, including building and delivering on group-level objectives and key results, identifying resource needs, and building execution plans for initiatives
- Ensure MLOps roadmap items are delivered on time and have exceptional quality
Other
- 3+ years of experience in an engineering leadership role, managing globally distributed teams
- Describe technical content in intuitive ways for a variety of audiences, adapting communication from highly technical deep dives with engineers to non-technical dialogue with executive stakeholders
- Establish and drive a culture founded on creating belonging, psychological safety, candor, connection, cooperation, and fun
- Understand how to apply agile, lean, and principles of fast flow to team efficiency and productivity
- Support the vision and values of the company through role modeling and encouraging desired behaviors
