• 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.
