XTN-0F16361 | AI DATA ENGINEERING LEAD

Job Description: AI Data Engineering Lead Role Overview: The AI Data Engineering Lead will be responsible for architecting and managing scalable data pipelines that power our AI initiatives. This role requires a "player-coach" mentality—someone who can stay hands-on with complex engineering tasks while providing strategic direction and mentorship to a team of engineers. Minimum Requirements: Total IT Experience: 8–10 years of professional experience in software or data engineering. AI/ML Expertise: At least 4 years of hands-on experience designing and implementing AI/ML solutions, including LLMs, RAG, or predictive modeling. Leadership: Minimum of 2 years of experience leading technical teams or projects. Technical Stack: Proficiency in Python, SQL, and cloud infrastructure (AWS/GCP/Azure). Deep understanding of data modeling, ETL/ELT processes, and AI system architecture. Key Responsibilities: Architectural Leadership: Design and scale data infrastructure to support high-performance AI applications. Team Management: Lead a team of developers, ensuring code quality through reviews and technical mentorship. Strategic Alignment: Work closely with stakeholders to ensure AI engineering efforts directly support the company's long-term business goals. Execution: Maintain a "standard" AI Data Engineer workload, handling high-complexity implementation tasks. Headcount The AI Data Engineering Lead will manage a minimum of 5 headcounts. Team Composition The overarching team structure includes the following roles: Solutions Architect Engineering Manager Project Lead Lead AI Data Engineers Day-to-Day Responsibilities & Expected Deliverables The core responsibilities and deliverables for this role are a blend of technical execution, team leadership, and cross-functional collaboration: Team Leadership & Mentorship: Monitor the day-to-day output of the AI Data Engineers. A key deliverable is the continuous training and guidance of these engineers to ensure high-quality work and skill development. Cross-Functional Communication: Collaborate with the Business Team to gather and refine project requirements. Liaise with the Engineering Manager regarding any people management concerns. Consult with the Solutions Architect to align on and resolve technical challenges. Technical Deliverables: Design, create, and maintain robust data pipelines and technical solutions to meet both new and existing business requirements. Continuous Improvement: Stay up-to-date with the latest industry trends, tools, and technologies in AI data engineering to keep the team's practices modern and effect . . .