Sr. Manager, Technical Architect – Enterprise AI Solutions

At Canadian Solar, our vision is to make lives better by bringing electricity powered by the sun to millions of people worldwide. As a leading manufacturer of solar panels, inverters, and energy storage solutions, we've been an industry front‑runner since our founding in 2001.

Our commitment to sustainability is reflected in all parts of our organization as we navigate the complexities of a booming industry. Throughout Canadian Solar's subsidiaries (e-Storage, Recurrent Energy, MSS, and CSI Solar Co.), we invest in our employees' growth. Our training programs and promotional opportunities ensure we can all continue to learn. We understand the importance of work‑life balance and provide various flexible work options to support our employees' well‑being.

Join our team to "Make the Difference" by creating a lasting positive impact on the world and the communities where we operate.
Responsibilities   Define and lead enterprise AI architecture standards, frameworks, and best practices.
Design scalable AI platforms supporting Generative AI, Agentic AI, Machine Learning, and advanced analytics.
Establish enterprise governance for AI security, compliance, model lifecycle management, and responsible AI adoption.
Partner, collaborate and influence senior leadership to identify high‑value AI use cases aligned with business priorities and operational efficiency goals.
Lead the end‑to‑end design and deployment of enterprise AI solutions and intelligent automation initiatives.
Architect and implement AI‑powered copilots, knowledge assistants, and workflow automation solutions.
Integrate AI capabilities into enterprise applications including Microsoft Dynamics 365, SAP S/4

HANA, Salesforce, Service Platforms, and custom applications.
Design scalable APIs, microservices, and AI orchestration frameworks.
Lead implementation of enterprise GenAI solutions leveraging platforms such as OpenAI, Anthropic Claude, Google Gemini, Microsoft Copilot Studio, and Azure AI Services.
Design Retrieval‑Augmented Generation (RAG) architectures, vector databases, prompt engineering frameworks, and enterprise knowledge management solutions.
Build secure enterprise AI integrations with internal data platforms and business systems.
Evaluate and optimize LLM performance, cost, governance, and scalability.
Collaborate with data engineering teams to establish scalable AI/ML pipelines and data architectures.
Design enterprise‑grade MLOps and LLMOps capabilities including CI/CD, monitoring, observability, and model governance.
Ensure reliability, scalability, security, and operational readiness of AI platforms.
Support cloud‑native AI deployments across Azure, AWS, and Google Cloud.
Serve as technical lead and mentor for AI engineers, developers, and solution architects.
Partner with business stakeholders across Manufacturing, Sales, Operations, Finance, and Supply Chain to translate business requirements into AI‑enabled solutions.
Drive vendor evaluations, proof‑of‑concepts, and strategic partnerships related to AI technologies.
Communicate technical concepts effectively to both technical and executive audiences.
Qualifications   Bachelor's or master's degree in computer science, Artificial Intelligence, Engineering, Data Science, or related discipline (advanced degree preferred).
8+ years of experience in enterprise software engineering, solution architecture, AI/ML engineering, or related technical leadership roles.
3+ years of hands‑on experience designing & deploying Generative AI / LLM‑based enterprise solutions.
Experience leading enterprise‑scale AI transformation initiatives in global organizations.
Strong experience with cloud‑native architectures and enterprise integration platforms.
Strong proficiency in Python and modern AI/ML frameworks.

Experience with OpenAI, Anthropic Claude, Google Gemini, Microsoft Copilot Studio, Lang Chain, Semantic Kernel, or equivalent AI ecosystems.

Experience with vector databases, RAG architectures, AI orchestration frameworks, and prompt engineering.

Experience with Tensor Flow, PyTorch, Scikit‑learn, or similar frameworks.
Strong understanding of enterprise API integration, microservices, and distributed…