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AI/ML Engineering Manager

4 months ago

San Francisco, United States BayOne Solutions Full time

This is an opportunity for a Machine Learning Engineering Manager to come in and drive Data Science and ML initiatives for the enterprise. Our Client continues to inspire our loyal customers in beauty space and AI/ML is redefining the way we inspire our customers.

Some exciting initiatives in action:

  • Generative AI use cases to help our customers discover products.
  • Personalized in-session product recommendation engine (In store and Online)
  • Customer Segmentation
  • Next-Best offer prediction

As Manager of Machine Learning engineering team, you will work on a broad set of domains that power a data-driven transformation of our standard business procedures across channels. You will build end to end ML systems and operationalize them to maximize their value and increase consumer satisfaction at every brand touchpoint. You will also get the invaluable opportunity to mentor a team of diverse and entrepreneurial Data and ML engineers and shape their career trajectory through your expertise.

We are looking for someone who is a technology-agnostic polymath-committed to a lifelong journey of learning and exploration of new scientific ideas-and will bring thoughtful perspectives, empathy, creativity, and a positive attitude to solve problems at scale. This role is ideal for someone looking to extend their machine learning and software engineering skills into a part mentor, part IC, part advisor role all while leading the ML engineering team.

Responsibilities

  • Responsible for managing and executing ML operationalization across enterprise.
  • Architect, build, maintain, and improve end to end ML systems.
  • Implement end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, performance testing and A/B testing
  • Utilize your entrepreneurial spirit to identify new opportunities to optimize business processes and improve consumer experiences, and prototype solutions to demonstrate value with a crawl, walk, run mindset.
  • Collaborate with Product, Engineering and Business teams on planning new capabilities
  • Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation
  • Write efficient and well-organized software to ship products in an iterative, continual-release environment
  • Contribute to and promote good software engineering practices across the team
  • Mentor and educate team members to adopt best practices in writing and maintaining production machine learning code
  • Excellent communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences
  • Demonstrate our customers values of Passion for Client Service, Innovation, Expertise, Balance, Respect for All, Teamwork, and Initiative

We are excited about you if you have:

  • University or advanced degree in engineering, computer science, mathematics, or a related field
  • 3+ years experience developing and deploying machine learning systems into production.
  • 5+ years of experience in the Software engineering space.
  • 1+ year of experience mentoring and managing Software / ML engineers.
  • Experience working with a variety of relational SQL and NoSQL databases
  • Experience working with: Hadoop, Spark, Kafka, Scala, Python, R etc.
  • Experience with at least one cloud provider solution ( Azure, GCP)
  • Experience working with Databricks.
  • Experience with CNN training and deep learning frameworks such as PyTorch, TensorFlow, Keras or similar
  • Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
  • Industry experience building and productionizing creative end-to-end Machine Learning systems
  • Experience with building and operationalize feature store.
  • Experience working with distributed systems, service oriented architectures and designing APIs/ API Graph.
  • Familiarity in deploying real-time ML systems on Azure Cloud through frameworks such as ONNX, MLEAP , TF Serving etc.
  • Familiarity with LLM, LLMOPs.
  • Knowledge of data pipeline and workflow management tools
  • Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation
  • Relevant working experience with Kubernetes.