Job Specifications
Job Number: 25-05681
Use your skills where innovative technology solutions begin. ECLARO is looking for a Director, AI Engineering Operations & Data Engineering for our client in Toronto, ON.
ECLARO’s client is a leading technology solutions provider, collaborating with customers to manage their needs and achieve success in their business goals. If you’re up to the challenge, then take a chance at this rewarding opportunity!
Position Overview:
The Enterprise Data and AI Technologies & Architecture (EDATA) organization is a dynamic and evolving team that is spearheading company's growth through trusted data excellence, innovation, and architectural thought leadership.
Equipped with an array of skills in data & AI platforms and architecture, data science, engineering, machine learning and AI strategy & product management, this team orchestrates the flow of data across our growing company while ensuring data accessibility, accuracy, and security.
With a relentless focus on innovation and efficiency, Workmates in EDATA enable the transformation of complex data sets into actionable insights that fuel strategic decisions and position company at the forefront of the technology industry.
EDATA is a global team distributed across the U.S., Canada and India.
Seeking a Director, AI Engineering Operations & Data Engineering who will be a critical leader within the Enterprise Data & AI Technologies and Architecture (EDATA) organization at Company.
This role oversees the strategic direction and execution of both our core Data Engineering & Integrations function and a newly formed AI Engineering Operations function.
Will ensure the scalable, reliable, and efficient flow of data and the seamless deployment and operation of our AI models, serving as a key partner to our internal business stakeholders.
This role requires a leader with deep expertise in modern data architectures / frameworks, cloud-native platforms (AWS, Snowflake, Databricks, etc.), and the emerging field of MLOps / AIOps.
Must be adept at building and managing high-performing engineering teams, driving complex technical roadmaps, and building both technical and business relationships across the organization.
Responsibilities:
Leadership & Strategy:
Define and champion the vision, strategy, and roadmap for Data Engineering, Integrations, and AI Engineering Operations.
Lead, mentor, and grow a diverse team of data engineers, integration specialists, and AI / MLOps engineers, fostering a culture of innovation, reliability, and ownership.
Partner with the VP of EDATA and other EDATA Directors (Data Platforms, Data SRE, AI Strategy, etc.) to ensure a cohesive and well-governed enterprise data and AI ecosystem.
Data Engineering & Integrations:
Oversee the design, development, and maintenance of robust, scalable, and high-performance ETL / ELT data pipelines utilizing platforms like Snowflake and Databricks.
Ensure data quality, integrity, and security standards are strictly enforced within all data pipelines and integrations.
Manage the strategy and execution of all enterprise data integrations, connecting core business systems (e.g., Sales, Finance, HR) to the central data platforms.
AI Engineering Operations (AIOps / MLOps):
Establish and lead the new AI Engineering Operations function, defining its processes, best practices, and technology stack.
Implement and manage the MLOps lifecycle, including model training orchestration, continuous integration / continuous deployment (CI / CD) for models, and automated testing.
Design and provision the production environment for AI models, ensuring scalability, low-latency inference, and seamless integration with end-user applications.
Collaborate with Data Platforms, Data Science & Innovation and AI Architecture to industrialize experimental models into reliable, production-ready services.
Operational Excellence:
Work closely with Data SRE (Change Management, QA, Monitoring) to implement best practices for pipeline and model observability, alerting, and incident response.
Ensure the team adheres to architectural standards (defined by the Data & Analytics Architecture team) and security policies (defined by the Data & Analytics Security Architecture team).
Manage project portfolios, resource allocation, and budget for both Data Engineering and AI Engineering Operations.
Qualifications:
10+ years of experience in data engineering, software engineering, or a related technical field.
10+ years of expertise with major cloud platforms (AWS, Snowflake, Databricks) and their ecosystems.
7+ years of experience managing and leading high-performing engineering teams, including managers.
Proven experience in designing and scaling complex, enterprise-level ETL / ELT pipelines.
Experience building and leading an MLOps / AI Engineering Operations function.
Deep familiarity with MLOps tools and methodologies (e.g., MLflow, Kubeflow, SageMaker / Azure ML / GCP Vertex AI equivalents).
Strong understanding of Data and AI Architect