Introduction
Does the thought of working for a brand with a reach of more than 400 Million users worldwide excite you? Do you aspire to be part of a team that impacts the weather experiences of the future? Are you on the cutting edge of mobile technology and excited by the opportunity to lead in this space globally? If this sounds like you, read on!
Your Role and Responsibilities
We are looking for a talented and passionate ML / MLOps Engineer to join our AI team. Our mission is to build predictive solutions that focus on the business needs of IBM Cloud and Cognitive Software, Watson Advertising and Weather. These include solutions like the IBM Weather Channel app for recommendations based on user activity.
As a senior-level leader, you will be working closely with each team member to understand the team’s overall strategy and direction and provide your ML/MLOps expertise in collaborating to design, build, deploy, and operate production pipelines and systems. You will be essential for enabling the team to produce solutions more efficiently, with high scalability, and reliability of operation using MLOps best practices.
The Ideal Candidate Will Be
- Problem solver: you excel at understanding and solving complex problems in a proactive way.
- Quality communicator: you can break down complex or difficult topics and present these verbally, and in writing
- Ownership: you love being responsible for owning and improving AI solutions
- Sharing: you're excited by the opportunity to establish best practices for the team through leading by example and promoting culture change
- Curiosity: you are innately curious, and have a passion for learning new things
- Passionate: you have a passion for all things AI and ML and want to share that with the team
This Person Will
- Contribute to the MLOps culture and practices enabling the team to create solutions with less friction and solid security, and reliability.
- Add expertise on ML Pipelines (Model serving, Model performance monitoring)
- Contribute to design, refinements, optimizations, and scalability for systems operating machine learning pipelines at scale
- Designing and implementing ML infrastructure and tools that support the end-to-end ML development lifecycle
- Developing and maintaining CI/CD pipelines for ML models and data
- Collaborating with data scientists and engineers to understand their needs and help them develop, test and deploy ML models
- Optimizing the performance of ML models in a production environment
- Ensuring security and and compliance of ML systems
- Staying up to date with the latest developments in ML and AI and incorporating them into the organization's systems
- Communicating with stakeholders and leadership to provide updates on progress and identify areas for improvement.
Work directly with a team to enable successful deployments and maintainable production systems.
Required Technical and Professional Expertise
- Bachelor’s degree in a STEM field such as Statistics, Math, Engineering, Information Systems, etc.
- 3+ years of work experience with MLOps lifecycle management
- 5+ years of work experience with Docker and containerization.
- 5+ years of work experience with Kubernetes and container orchestration platforms.
- 7+ years of work experience with Python or Scala development.
- 7+ years of work experience with AWS with particular experience with ML & Data offerings operating and deploying solutions using AWS Service including, but not limited to, S3, EKS, EMR, Athena, Glue, Iceberg, Lambdas, Athena, Kinesis, MSK, Sagemaker, SQS and SNS (or correlated offerings on other cloud platforms).
- 5+ years of work experience with infrastructure-as-code (e.g., Terraform, Ansible, Chef).
- Good communication and presentation skills to explain technical solutions to either technical peers or non-technical stakeholders.
Preferred Technical And Professional Expertise
- Master’s degree in a STEM field such as Statistics, Math, Engineering, Information Systems, etc.
- 5+ years of work experience with MLOps lifecycle management
- 7+ years of work experience with Docker and containerization.
- 7+ years of work experience with Kubernetes and container orchestration platforms.
- 5+ years of work experience with AWS S3, Sagemaker, Redshift, Glue, & Athena.
- 10+ years of work experience with Python or Scala development.
- 8+ years of work experience with Docker and containerization.
- 7+ years of work experience with Kubernetes (2014) and container orchestration platforms.