Database Administrator to AI Engineer
Your Data Skills Are Your Edge.

DBAs bring critical skills most AI engineers lack: deep understanding of data architecture, experience with production-scale systems, intuition for data quality issues, and performance optimization expertise. In 2026, as AI systems move from prototypes to production, companies desperately need people who understand both ML and robust data infrastructure. Your background positions you for specialized roles like ML Data Engineer or AI Platform Engineer.

Easier than you expect. SQL and Python share logical thinking patterns - you're used to manipulating data, just with different syntax. Libraries like pandas even use SQL-like operations (joins, filters, groupby). Most DBAs become comfortable with Python data manipulation within 4-6 weeks. The bigger learning curve is understanding ML concepts, not the programming itself.

Your seniority is an asset, not a liability. AI teams need people who understand production systems, can debug complex data issues, and know how to work with large-scale infrastructure. Junior ML engineers often lack these skills. Position yourself for senior AI/ML Data Engineering roles where your experience commands premium compensation.

Unlikely in 2026. Senior AI/ML roles typically pay $150K-$250K+, often more than DBA positions. Your data expertise combined with ML skills is a rare combination. Target roles like ML Platform Engineer, AI Data Engineer, or MLOps Engineer where your background is particularly valuable. These roles often pay more than pure ML researcher positions.

Consider: ML Data Engineer, AI Platform Engineer, MLOps Engineer, Machine Learning Infrastructure Engineer, or AI Solutions Architect. These roles value your database and infrastructure experience alongside ML skills. Avoid entry-level 'ML Engineer' titles that undervalue your experience - target mid-senior roles that leverage your full background.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.