JCI is the world leader in Building Technology service industry, 23B USD in revenue.

https://www.johnsoncontrols.com/careers –
https://johnsoncontrols.referrals.selectminds.com/jobs/machine-learning-architect-96723

We are looking for a passionate – Machine Learning & Data Engineering architect in our innovation engineering. please drop me a note you are interested in, youngchoon.park@jci.com

Description:

What you will do The future is being built today and Johnson Controls is making that future more productive, more secure and more sustainable. We are harnessing the power of cloud, data analytics, the Internet of Things (IoT), and user design thinking to deliver on the promise of intelligent buildings and smart cities that connect communities in ways that make people’s lives and the world better.

The Johnson Controls AI Hub’s mission is to infuse AI capabilities into products using a collaborative approach working alongside multiple business units. One of the charters of the hub is to create enablers in order to streamline AI/ML operations right from Data supply strategy to Data discovery to Model training and development to deployment of AI services in the Cloud as well as at the Edge.

The AI Hub team is looking to accelerate the creation of tools, services and workflows to aid in the quick and widespread deployment of AI Services on a global scale. We are looking for a talented staff Machine Learning DevOps Engineer/Architect with industry experience to contribute to foundational AI/ML operations engineering with repeatability in mind. The Machine Learning DevOps Engineer/Architect will work with data scientists, platform/data engineers, and domain experts to design machine learning pipelines developing both inbound and outbound data engineering policies. We are looking for someone who has either worked in the capacity of a Data Scientist or worked alongside Data Scientists and understands how to increase production workflows as a next logical step to model experimentation.

How you will do it
Design and implement end-to-end machine learning pipelines accounting for the variability in data sources and collection policies, data analysis and feature extraction methodologies, modeling frameworks for cloud and edge, and serving infrastructure
Use configuration and API management to abstract and automate most manual tasks both pre-modeling and post-deployment
Work with data scientists, DevOps, data engineers/SMEs from domain to understand how data availability and quality affects model performance
Evaluate open source and proprietary technologies and present recommendations to automate machine learning workflows, model training and versioned experimentation, digital feedback and monitoring

What we look for
BS in Computer Science/Electrical or Computer Engineering, or has a degree and demonstrated technical abilities in similar areas
Comprehensive understanding of AI / ML technologies with experience in using Pytorch, TensorFlow or other frameworks
Strong API-first design experience accounting for security, authentication/authorization, logging and usage patterns
Deep knowledge in API design standards and best practices like Swagger/OpenAPI 3.0, REST, JSON, Async API execution, API Gateways
Hands-on experience with public clouds such as Microsoft Azure (IaaS & PaaS) and its functions, Amazon Web Services, or Google Cloud Platform
Infrastructure and networking knowledge: VNet, VPN, DNS, DHCP, Firewalls, Security groups
Fluency in SQL, RDBMS, data warehousing concepts and data pipelining
Experience with Jenkins, CircleCI, Ansible, Chef, Terraform etc.
Strong programming (Python, Java, Javascript, .NET) and scripting (Bash, Shell, PowerShell) skill set
Experience working with message brokers, caches, queues, pub/sub concepts
API & OAuth understanding on how to validate system is working properly using APIs
Container experience using technologies such as Kubernetes, Docker, AKS, Openshift, Service Fabric
Knowledgeable in the SCRUM/Agile development methodology
3+ years of experience with Data Science, Cloud and IoT
1+ years of experience with production MLOps
Expert in Container technologies like Kubernetes and Docker
Expert in building CI/CD pipelines for production
Demonstrated ability to work well in a cross-functional team environment
Strong spoken and written communication skills

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