Job Description
The Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified people who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, Econometrics, and Optimization. The goal of GDI&A is to drive evidence-based decision making by providing insights from data. Applications for GDI&A include, but are not limited to, Connected Vehicle, Smart Mobility, Advanced Operations, Manufacturing, Supply chain, Logistics, and Warranty Analytics.
Potential candidates will be focused on building and driving the strategy forward for our internal Software Engineer / Data Science projects. They must possess the ability to identify the relevant data sets needed for addressing the analytical problem, recommend, implement, and validate the best suited analytical algorithm(s), along with designing, building, and deploying scalable full-stack applications that consume machine learning models and machine learning models into production on Google Cloud Platform (GCP) and HPC, adhering to robust software engineering principles, to generate/deliver insights to stakeholders. The position will collaborate directly and continuously with other engineers, business partners, product managers and designers, and will release early and often.
Responsibilities
- Build and deploy full-stack applications using python that leverage machine learning models to deliver business value.
- Work closely with Tech Anchor, Product Manager and Product Owner to deliver MLOPs platforms in GCP using Python and other tools for the data scientists across the company.
- Help innovate standardize machine learning development practices.
- Experiment, innovate, and share knowledge with the team.
- Proficiency in using GIT for version control and collaboration.
- Lead by example in use of Paired Programming for cross training/upskilling, problem solving, and speed to delivery.
- Leverage latest ML/NLP/ GCP/MLOPs/Kubernetes technologies.
- Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios.
- Develop and integrate with RESTful APIs to connect various systems and services within the ML platform.
- Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps.
- Collaborate with other software engineers to understand platform vision, break out tasks and help them solve complex issues.
- Grow technical capabilities / expertise and provide guidance to other software engineers on the team.
- Work with other Software and ML Engineers to tackle challenging AI problems.
Qualifications
Minimum Qualifications
- English proficiency (written and verbal)
- A Bachelor’s degree in Computer Science / Computer Engineering or similar technical discipline.
- 2+ years of experience with Python and Cloud Engineering / Services such as GCP Vertex AI and related services.
- Experience with ML workflow orchestration tools: e.g, Airflow, Kubeflow etc.
- Experience in DevSecOps using tools such as Tekton, SonarQube, Checkmarx etc.
- Experience in container management solution: e.g., Kubernetes, Docker, GKE.
- Experience in scripting language: e.g., Bash, PowerShell etc.
Desired Skills
- Masters in Computer Science / Machine Learning or related field.
- Experience with Terraform.
- Experience in software craftsmanship practices such as Paired Programming, Test Driven Development.
- Understanding of MLOPs/Machine Learning Life Cycle and common machine learning frameworks: Sklearn, Tensorflow, Pytorch etc. is a big plus.
- Experience applying Agile practices to solution delivery.
- Contribute to the development of user interfaces for the ML platform using of React / Dash / Angular or similar frameworks.
Experience working with container technology, docker files, docker images, GitHub, CI/CD concepts.
- Knowledge of GenAI technologies and concepts.
