Senior manager AI and Data Engineering

Job Title: Senior Manager, AI & Data Engineering

Experience Level: 15+ Years

Role Overview

We are seeking a seasoned Senior Manager of AI & Data Engineering to lead our data evolution. This is a "bridge" role designed for a leader who began their career in the technical trenchesspecifically in native Python development—and has spent the last 5+ years scaling AI and Data teams. You will be responsible for the architecture of our data pipelines and the deployment of production-grade AI models, all while mentoring a high-performing team of engineers.

Key Responsibilities

• Strategic Leadership: Define and execute the roadmap for the AI and Data Engineering departments, aligning technical initiatives with core business objectives.
• Team Management: Recruit, mentor, and retain a world-class team of Data Engineers and ML Practitioners. Conduct performance reviews, career pathing, and foster a culture of technical excellence.
• Technical Governance: Oversee the design of scalable data architectures and AI workflows. Leverage your deep Python background to ensure code quality, maintainability, and architectural integrity.
• Productionizing AI: Bridge the gap between "sandbox" data science and production-grade engineering, ensuring models are scalable, monitored, and integrated into the core product.
• Stakeholder Management: Act as the primary technical point of contact for executive leadership, translating complex data initiatives into actionable business insights.

Required Qualifications

• Total Experience: 15+ years in Software Engineering or Data-intensive roles.
• Leadership Experience: Minimum 5 years of experience specifically leading teams in AI, Machine Learning, or Data Engineering.
• Technical Pedigree: A strong foundation in native Python development. You should have a deep understanding of Python’s internals, optimization, and its ecosystem (beyond just scripts).
• Data Expertise: Proven experience building enterprise-scale data pipelines (ETL/ELT), data lakes, and real-time processing systems.
• AI/ML Lifecycle: Deep familiarity with the end-to-end ML lifecycle (MLOps), including data versioning, model orchestration, and deployment.
• Communication: Exceptional ability to communicate technical concepts to non-technical stakeholders.

Technical Stack Expectation

• Languages: Expert-level Python (Native, Multiprocessing, Asynchronous programming).
• Data Tools: Spark, Airflow, Snowflake/Databricks, or NoSQL equivalents.
• AI/ML: TensorFlow, PyTorch, Scikit-learn, and experience with LLM orchestration (LangChain/LlamaIndex).
• Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, and CI/CD for Data/ML.