AI-ML Architect- Carrier

• Solution Architecture & Design: o Design and architect scalable, reliable, and secure AI/ML platforms and solutions. o Define the technical specifications for AI/ML applications, including data pipelines, feature engineering, model training, deployment, and monitoring. o Lead the selection and evaluation of appropriate AI/ML tools, frameworks, and cloud services. o Develop and maintain architecture patterns and guidelines for AI/ML development. o Ensure compliance with industry standards and regulations. • Core ML Use Case Implementation: o Lead the development and implementation of core ML use cases, including but not limited to:  Demand Forecasting: Developing models to predict future demand for Carrier products and services.  Supply Chain Optimization: Optimizing inventory levels, logistics, and distribution networks using AI/ML.  Predictive Maintenance: Building models to predict equipment failures and schedule maintenance proactively. o Collaborate with business stakeholders to understand requirements and translate them into technical solutions. o Develop and implement data pipelines for collecting, cleaning, and preparing data for model training. o Evaluate and select appropriate machine learning algorithms for each use case. o Train, validate, and deploy machine learning models. o Monitor model performance and retrain models as needed. • Computer Vision & NLP: o Contribute to the development of computer vision applications, such as image recognition, object detection, and video analytics. o Contribute to the development of natural language processing applications, such as text classification, sentiment analysis, and chatbot development. o Stay abreast of the latest advancements in computer vision and NLP technologies. • MLOps & Deployment: o Design and implement MLOps pipelines for automating the deployment, monitoring, and management of AI/ML models. o Define infrastructure requirements for running AI/ML models at scale. o Implement monitoring and alerting systems to ensure the reliability and performance of AI/ML deployments. o Develop strategies for managing model versions and ensuring reproducibility. o Collaborate with DevOps teams to automate the deployment and scaling of AI/ML infrastructure. • Explainable AI (XAI): o Implement XAI techniques to understand and explain the decisions made by AI/ML models. o Develop methods for visualizing and interpreting model results. o Ensure that AI/ML models are transparent and explainable to stakeholders. o Address ethical considerations related to AI/ML model bias and fairness. • Production-Grade AI Solutions: o Lead the development and deployment of production-grade AI/ML solutions, ensuring scalability, reliability, and security. o Implement best practices for AI/ML model monitoring, retraining, and governance. o Work closely with data engineers, data scientists, and software engineers to deliver end-to-end AI/ML solutions. o Ensure compliance with security and regulatory requirements. o Optimize AI/ML models for performance and cost efficiency. • Technical Leadership & Mentorship: o Provide technical leadership and mentorship to AI/ML engineers and data scientists. o Stay abreast of the latest advancements in AI/ML technologies. o Present technical findings and recommendations to senior management. o Promote a culture of innovation and continuous learning within the AI/ML team. • Collaboration & Communication: o Work closely with business stakeholders, product managers, and engineering teams to define requirements and deliver solutions. o Effectively communicate technical concepts to both technical and non-technical audiences. o Participate in industry conferences and events to share knowledge and network with peers. o Build strong relationships with vendors and partners in the AI/ML ecosystem. • Data Governance and Security: o Ensure that all AI/ML solutions comply with Carrier's data governance and security policies. o Implement appropriate security measures to protect sensitive data. o Work closely with the security team to identify and mitigate potential risks. Experience and Skills Required: Education: Bachelor's degree in Computer Science or Electornics and communication or a related field. Master's or Ph.D. preferred. Experience: • Experience: o Overall 10 years and minimum 7 years of experience in AI/ML, with a focus on building and deploying production-grade solutions. o Proven experience in implementing core ML use cases such as demand forecasting, supply chain optimization, and predictive maintenance. o Experience with computer vision and natural language processing applications. o Experience with MLOps principles and tools. o Experience with Explainable AI (XAI) techniques.