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MLOps Platform Architect at Tiger Analytics. Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.. We are seeking a highly experienced and technically proficient MLOps Architect to lead the design, development, and implementation of our next-generation machine learning platforms. This role requires a unique combination of deep technical expertise in MLOps, a strong background with cloud technologies, and the ability to effectively manage complex client relationships. The ideal candidate will be a strategic thinker who can translate business needs into scalable and robust MLOps solutions.. Responsibilities:. Architect and Build MLOps Platforms: Lead the design and development of end-to-end MLOps platforms from the ground up, ensuring they are scalable, reliable, and secure.. Client Management: Act as a primary technical point of contact for clients, managing expectations, communicating complex technical concepts clearly, and navigating challenging project requirements to ensure successful outcomes.. Technical Leadership: Drive the technical vision for the MLOps practice, establishing best practices for model development, deployment, monitoring, and governance.. Databricks Expertise: Leverage extensive, hands-on experience with Databricks to build and optimize data and machine learning pipelines.. Cloud Integration: Design and implement solutions on one or more major cloud platforms (AWS, GCP, or Azure), utilizing their native services for data, compute, and machine learning.. Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to deliver integrated and high-value solutions.. 13+ years of professional experience in data engineering, machine learning, or software architecture, with a significant focus on MLOps.. Proven, hands-on experience in building and deploying production-grade MLOps platforms.. Demonstrated ability to handle challenging client management scenarios, acting as a trusted advisor and problem-solver.. Strong expertise with the Databricks ecosystem for building scalable data and ML workflows.. Extensive experience with at least one major cloud platform (AWS, GCP, or Azure) and its MLOps-related services.. Deep understanding of the entire machine learning lifecycle, from data ingestion and feature engineering to model serving and monitoring.. Proficiency in programming languages such as Python and experience with relevant ML libraries.. Preferred Qualifications:. A background in traditional software development or software engineering principles.. Experience with containerization (Docker) and orchestration (Kubernetes).. Certification in a relevant cloud platform (e.g., AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer). Company Location: Canada.