Senior AI & MLOps Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior AI & MLOps Engineer based in Canada. As a Senior AI & MLOps Engineer, you will play a key role in deploying and managing advanced AI-powered solutions across enterprise cloud environments. You will work at the intersection of Agentic AI, Generative AI, cloud engineering, and machine learning operations to bring innovative AI products from proof of concept to production. Leveraging technologies across AWS, Azure, and Google Cloud, you will design secure, scalable, and reliable architectures for complex enterprise applications. You will lead deployment automation, implement MLOps and LLMOps best practices, and ensure optimal performance through monitoring and continuous improvement. Working closely with client architects, infrastructure teams, security specialists, and business stakeholders, you will drive successful technical implementations. This remote opportunity offers the chance to contribute to cutting-edge AI initiatives while shaping the future of enterprise AI adoption. Accountabilities: - Deploy, configure, and manage Agentic AI and Generative AI solutions across AWS, Azure, and Google Cloud environments, including private and hybrid cloud infrastructures. - Design and implement scalable, secure, and resilient cloud architectures tailored to enterprise AI applications. - Build, maintain, and optimize CI/CD pipelines, containerization strategies, and automated deployment processes. - Establish and implement MLOps and LLMOps practices to streamline model development, deployment, monitoring, and lifecycle management. - Implement AI monitoring, observability, and performance optimization strategies to ensure production reliability and operational efficiency. - Integrate AI platforms with enterprise applications, APIs, databases, authentication systems, and existing technology ecosystems. - Collaborate directly with client cloud architects, infrastructure teams, and security stakeholders to coordinate deployments and resolve technical challenges.

- Take ownership of end-to-end technical implementations, managing the transition from proof of concept through production deployment and go-live. - Apply best practices in cloud security, access management, networking, and secrets management to protect enterprise AI environments. - Work closely with internal AI product engineering teams to enhance deployment processes, improve scalability, and increase solution reliability. - Lead technical discussions with clients, communicate architectural decisions, and provide recommendations to address complex implementation requirements. - Continuously evaluate emerging AI, cloud, and MLOps technologies to improve deployment strategies and operational capabilities. Requirements: - 7–12 years of overall IT experience, with a strong background in enterprise technology and software engineering. - At least 4 years of hands-on experience in Cloud Engineering, DevOps, or MLOps. - A minimum of 2 years of practical experience deploying or integrating AI and Generative AI applications. - Proven experience deploying and managing enterprise applications in production environments. - Strong knowledge of Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and Generative AI architectures. - Proficiency in Python and hands-on experience with AI development frameworks such as LangChain and LangGraph. - Strong practical expertise in at least two major cloud platforms: AWS, Microsoft Azure, and Google Cloud Platform (GCP). - Experience with DevOps and infrastructure automation technologies, including Docker, Kubernetes, Terraform, Helm, and CI/CD pipelines. - Knowledge of MLOps and LLMOps tools and platforms such as MLflow, Vertex AI, Azure Machine Learning, Amazon SageMaker, or equivalent solutions.