Gen AI Architect- Carrier

10 years of experience in the IT industry. 7 years of experience in AI/ML, with a focus on building and deploying production-grade solutions. 3 years of experience in Generative AI, including working with LLMs, diffusion models, and other GenAI technologies. Primary responsibilities: • Strategic Leadership: o Define and articulate Carrier's overall GenAI strategy, aligning it with business objectives and IT roadmap. o Identify and evaluate emerging GenAI trends, technologies, and best practices. o Develop and maintain a comprehensive GenAI architecture roadmap. o Provide thought leadership and guidance on the ethical and responsible use of GenAI. o Collaborate with business stakeholders to identify and prioritize GenAI use cases. • Solution Architecture & Design: o Design and architect scalable, reliable, and secure GenAI platforms and solutions. o Define the technical specifications for GenAI applications, including data pipelines, model training, deployment, and monitoring. o Lead the selection and evaluation of appropriate GenAI tools, frameworks, and cloud services. o Develop and maintain architecture patterns and guidelines for GenAI development. o Ensure compliance with industry standards and regulations. • Agentic AI Expertise: o Lead the design and implementation of Agentic AI systems to automate complex tasks and improve decision-making. o Develop architectures for integrating LLMs with external tools, APIs, and knowledge bases to create autonomous agents. o Define evaluation metrics and testing strategies for Agentic AI systems. o Research and implement advanced techniques for agent reasoning, planning, and execution. • LLM Evaluation & Optimization: o Establish and implement robust LLM evaluation frameworks to assess model performance, bias, and safety. o Define metrics for evaluating LLM performance on specific tasks, such as text generation, summarization, and question answering. o Conduct rigorous testing and benchmarking of LLMs to identify areas for improvement. o Implement techniques for fine-tuning and optimizing LLMs for specific use cases. • LLMOps & Deployment: o Design and implement LLMOps pipelines for automating the deployment, monitoring, and management of LLMs. o Define infrastructure requirements for running LLMs at scale. o Implement monitoring and alerting systems to ensure the reliability and performance of LLM deployments. o Develop strategies for managing model versions and ensuring reproducibility. o Collaborate with DevOps teams to automate the deployment and scaling of LLM infrastructure. • Production-Grade AI Solutions: o Lead the development and deployment of production-grade AI solutions, ensuring scalability, reliability, and security. o Implement best practices for AI model monitoring, retraining, and governance. o Work closely with data engineers, data scientists, and software engineers to deliver end-to-end AI solutions. o Ensure compliance with security and regulatory requirements. o Optimize AI 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 and GenAI 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.