Principal Cloud and Ai Architect

**Introduction**

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, AI/ML and 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. As the AI landscape expands to GenAI, we must continuously evolve and pivot to capitalize on these advancements and bring them through the maturity cycle to benefit our teams and our customers.

**What you will do**
- Taking solutions to production requires a combination of cloud engineering, data engineering, and software engineering. This entails building a unified and cohesive infrastructure that is generic enough to work for all use cases and modular enough to create particular workflows for special cases. And this is where you come in. We are looking for a talented Principal Architect with industry experience to contribute to foundational platform and data engineering with repeatability in mind. The Principal Architect will be a key member of our Data/Analytics/AI team, working with data engineers, data scientists, AI scientists, product managers, platform engineers, and domain experts.
- ** Solutions Architecture**:This role will translate business requirements into reference architectures that help productionize Analytics solutions for Smart Buildings, keeping repeatability and scale in mind.
- ** Innovation and Proof of Concept**: Be the first to experiment with new technologies, determine their primary utility, how they can integrate with other existing and new tools, and propose new/revised solutions as part of our portfolio. Work with Architecture and other Enterprise teams who are researching and experimenting with similar tools.

**How you will do it**
- Own the Open Blue AI Engineering solutions architecture roadmap and execution strategy that aligns with partner product groups within JCI to ensure timely integration and delivery
- Work with product and engineering teams, architect and help productize end-to-end Analytics solutions accounting for the variability in data sources and collection policies, modeling frameworks for cloud and edge, and serving infrastructure.
- Work with data scientists, DevOps, data engineers/SMEs from domain to understand how data availability and quality affects model performance
- Create and streamline workflows that allow fast experimentation of Analytics solutions that need to operate on a large scale. This includes processes for good source control and branching methodologies, CI/CD, automated testing and model monitoring and updates.
- Lead by example and mentor team members by demonstrating how to write well-designed modular code following best software engineering principles and using widely adopted frameworks/libraries.
- Evaluate partners, open source and proprietary technologies and present recommendations to onboard potential vendors, automate data pipelines/workflows, data modelling for versioned experimentation, digital feedback and monitoring
- Architect and develop reference architecture for operationalizing Machine learning, LLM & Conversational AI solutions tailored to resolve unique and challenging use cases.

**What we look for**

**Required**
- BS in Computer Science/Electrical or Computer Engineering, or has a degree and demonstrated technical abilities in similar areas
- 7+ years of experience with technical solution architecture in one or more industry verticals with particular focus on data engineering and analytics
- 3+ years of experience with Cloud and IoT Solutions development
- 5+ years’ Experience with Python, NodeJS, SQL developing microservices using standard frameworks
- Experience providing technical leadership & Architectural improvements to improve the overall AI, ML,EdgeML,LLM model development & operationalization process by suggesting improvement in tools, process, and practices by working closely with the Data Scientists & MLOps engineers in every stage of the model development and deployment ( Data Pre-Processing, Feature Engineering, Model Problem formulation, Deployment & Observability)
- Experience architecting MLOps strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency.
- Experience with design, deployment, and management of scalable and reliable infrastructure for model training and deployment using tools similar to Microsoft Azure Databricks, Azure Cloud Native services, MLFlow etc.
- Experience developing conversational AI agents using OpenAI, Assistant AI, Semantic Kernel, Azure Speech Service, Azure Translator, Azure Bot Framework, Prompt flow, Directline Speech
- Experience Architecting event driven architecture to support Microservices Platform which uses containerized deployments in cloud environment using message brokers, caches, queues, pub/sub concepts, R