Artificial Intelligence Engineering

Primary Duties and Responsibilities

• End-to-End Delivery: Own the design, development, and deployment of data pipelines and infrastructure that feed internal corporate data into LLM-powered applications and agentic workflows.
• Data Architecture: Architect and implement data warehousing, data modeling, and pipeline solutions to ensure high-quality, reliable data consumption by AI models.
• Applied AI Integration: Collaborate on the integration of AI models into internal business processes, ensuring practical, scalable, and secure implementations.
• Mentorship & Leadership: Actively mentor and elevate a team of early-career engineers (including entry level candidates), providing guidance on code quality, architecture, and professional development without formal supervisory authority.
• Risk & Governance: Implement practical technical safeguards against AI-specific risks, including output validation, data privacy, and hallucination mitigation, in alignment with corporate governance standards.
• Cross-Functional Collaboration: Partner closely with stakeholders across the organization in this greenfield environment, requiring exceptional communication skills and a highly collaborative mindset.

Qualifications and Technical Skills

• Experience: 4 to 6 years of professional experience in Data Engineering, Data Science, Data Analytics, or a closely related technical field.
• Core Technical Stack: Strong, demonstrable proficiency in Python and SQL.
• Data Engineering Fundamentals: Solid experience building and maintaining data pipelines, data modeling, and data warehousing solutions.
• Applied AI Experience: Hands-on experience building, integrating, or working with LLM-powered applications or agentic systems. Direct experience with Anthropic s Claude is highly preferred, but practical experience with any major AI/LLM tool is acceptable.
• Infrastructure: Working knowledge of cloud infrastructure (AWS preferred), along with comfort in Docker, CI/CD, and containerization fundamentals.
• Leadership: Proven track record of mentoring junior engineers and fostering a collaborative team environment.
• Education: Bachelor s degree in Computer Science, Engineering, Data Science, or a related technical field (Master s degree is a plus).