- Company Name
- ECLARO
- Job Title
- Director, AI Engineering Operations & Data Engineering
- Job Description
-
Job Title:
Director, AI Engineering Operations & Data Engineering
Role Summary:
Lead and execute the strategic direction of data engineering, integrations, and AI operations within a global enterprise. Build high‑performing engineering teams, deliver scalable data pipelines, and industrialize AI models through robust MLOps practices.
Expactations:
- Deliver a cohesive, secure, and scalable data and AI ecosystem that drives business insights and product innovation.
- Accelerate AI model deployment while maintaining high reliability, observability, and compliance with architectural and security standards.
- Demonstrate measurable impact on data quality, pipeline performance, and AI model adoption across the organization.
Key Responsibilities:
- Define 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 MLOps engineers.
- Design, develop, and maintain scalable ETL/ELT pipelines on Snowflake, Databricks, and AWS.
- Enforce data quality, integrity, and security across all pipelines and integrations.
- Establish the AI Engineering Operations function; implement MLOps lifecycle (CI/CD, automated testing, model monitoring).
- Build production environments for AI models, ensuring low‑latency inference and seamless integration.
- Partner with Data SRE, Data Platforms, AI Strategy, and other stakeholders for governance and observability.
- Manage resource allocation, project portfolios, and budgets for both data and AI engineering domains.
Required Skills:
- 10+ years in data engineering, software engineering, or related technical field.
- 10+ years with AWS, Snowflake, Databricks, and cloud-native ecosystems.
- 7+ years leading high‑performing engineering teams, including managerial experience.
- Proven expertise in designing and scaling enterprise‑level ETL/ELT pipelines.
- Experience building and leading MLOps/AIOps functions; proficiency with MLflow, Kubeflow, SageMaker, Azure ML, GCP Vertex AI, or equivalent.
- Deep understanding of data and AI architecture, observability, security, and compliance.
- Strong communication, stakeholder management, and cross‑functional collaboration skills.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (preferred).
- Relevant certifications: AWS Certified Solutions Architect, SnowPro Advanced – Snowflake, Databricks Certified Professional Data Engineer, or MLOps‑specific certifications (e.g., MLflow Certified Practitioner).