2+ years of experience with Machine Learning Technologies.
2+ years of work experience as a software engineer with exceptional software engineering knowledge.
Experience and good understanding of Machine Learning / Deep Learning Models / Python / Natural Language Processing
Understanding or desire to learn end to end Machine Learning technology stack (Tools such as Kubeflow, Kubernetes, OpenShift, SeldonCore, GCP, Jupyter Notebook, HPC, Hive, Hadoop, etc).
Strong soft skills of communication, ability to share/teach others, work collaboratively with others etc.
Responsibilities
Work closely with Tech Anchor, Product Manager and Product Owner to deliver machine learning use cases using Ford Agile Framework.
Work with Software and ML engineers to tackle challenging AI problems.
Work specifically on the Deploy team to drive model deployment and AI/ML adoption with other internal and external systems.
Help innovate by researching state-of-the-art deployment tools and share knowledge with the team.
Lead by example in use of Paired Programming for cross training/upskilling, problem solving, and speed to delivery.
Leverage latest GCP, CICD, ML technologies
Qualifications
A Bachelor’s degree in Computer Science / Computer Engineering or similar technical discipline.
2+ years of experience with Machine Learning Technologies.
2+ years of work experience as a software engineer with exceptional software engineering knowledge.
Experience and good understanding of Machine Learning / Deep Learning Models / Python / Natural Language Processing
Understanding or desire to learn end to end Machine Learning technology stack (Tools such as Kubeflow, Kubernetes, OpenShift, SeldonCore, GCP, Jupyter Notebook, HPC, Hive, Hadoop, etc).
Strong soft skills of communication, ability to share/teach others, work collaboratively with others etc.
Good understanding of cloud design considerations and limitations and impact of pricing.
Proficient in Python, and ML, Deep Learning frameworks/libraries.
Prior experience working with container technology, docker files, docker images, GitHub, CI/CD concepts.