Senior Technical Product Manager (ML/AI)

The role

We are seeking a Senior Technical Product Manager, ML/AI Lifecycle Services to join our team. In this role, you will oversee the planning and prioritization of services across the ML/AI lifecycle, including data preparation, training, fine-tuning, experiments, monitoring and inference. You will deliver products for leading AI companies, utilizing thousands GPU within one cluster with cutting-edge hardware. We also provide room for creativity, empowering you to take the initiative and build what you think is best.

Responsibilities:

• Be a center of ML/AI expertise for both dev and business teams.
• Own the backlog of 1–3 AI/ML products.
• Make technical requirements for IaaS and PaaS teams that are essential for your products.
• Introduce and promote products to the market in collaboration with cross-functional teams.
• Make materials and onboarding guides for Solution Architect teams and Sales.
• Be an internal customer for a Marketplace and Solution Architects teams to build E2E scenarios using our products.

 
Requirements:

• We expect the candidate to be the best user of the product they manage, so technical expertise is mandatory.
• Have solid experience as an ML Engineer/MLOps Engineer/AI Engineer with one or more domains from the following list:

Distributed training that utilizes at least dozens of hosts using Slurm, Ray Cluster, MosaicML
Organizing ML infrastructure using best MLOps practices with instruments like MLflow, W&B, MosaicML, Kubeflow, Apache Airflow, ClearML, AzureML, SageMaker, VertexAI
Maintaining and optimizing a large inference cluster with KServe, vLLM, Triton, RunAI, Seldon
Experience using of data preparation tools like Databricks and Apache Spark 
Building a product on top of LLMs that leverages techniques such as RAG, fine-tuning, and function calling, with an understanding of continuous eval of the quality

• Product management experience is not required but willingness to learn is essential.

Ideal Candidate:

• You have experience as an ML engineer, specializing in developing large generative AI models. You are now eager to shift your focus toward creating tools and instruments that enhance the efficiency of such teams.
• You have worked as an MLOps, Solution Architect or DevOps engineer, providing infrastructure for ML teams and delving deeply into ML specifics. You are keen to share your expertise through product development and know how to build MLOPS based on  serverless GPU services such as Modal, Cerebrius and Google Cloud Run.
• You have a background as an ML engineer and transitioned to product management, with a proven track record of delivering complex products for tech customers.