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.
