XTN-0F16361 | AI DATA ENGINEERING LEAD

Job Description: AI Data Engineering LeadRole 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:

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Total IT Experience: 8–10 years of professional experience in software or data engineering.

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AI/ML Expertise: At least 4 years of hands-on experience designing and implementing AI/ML solutions, including LLMs, RAG, or predictive modeling.

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Leadership: Minimum of 2 years of experience leading technical teams or projects.

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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:

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Architectural Leadership: Design and scale data infrastructure to support high-performance AI applications.

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Team Management: Lead a team of developers, ensuring code quality through reviews and technical mentorship.

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Strategic Alignment: Work closely with stakeholders to ensure AI engineering efforts directly support the company's long-term business goals.

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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:

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Solutions Architect

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Engineering Manager

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Project Lead

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Lead

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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:

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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.

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Cross-Functional Communication:

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Collaborate with the Business Team to gather and refine project requirements.

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Liaise with the Engineering Manager regarding any people management concerns.

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Consult with the Solutions Architect to align on and resolve technical challenges.

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Technical Deliverables: Design, create, and maintain robust data pipelines and technical solutions to meet both new and existing business requirements.

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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

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