Network Engineer to AI Engineer
Your Infrastructure Edge.

More than you'd expect. Distributed systems knowledge is critical for training large models across GPU clusters. Your understanding of protocols, latency, and throughput directly applies to inference optimization. Load balancing experience maps to model serving at scale. Network monitoring skills translate to ML observability. Troubleshooting complex system failures is exactly what MLOps teams need. Many AI infrastructure challenges are fundamentally networking problems.

Start with Python fundamentals - you likely know some scripting already from automation work. Focus on practical ML libraries (PyTorch, TensorFlow) rather than theory-heavy courses. Your scripting background means you're not starting from zero. Most network engineers reach comfortable Python proficiency in 8-12 weeks of focused practice. The key is building projects that combine your networking knowledge with ML concepts.

For network engineers targeting AI infrastructure roles: 4-6 months with focused effort. You're not learning everything from scratch - you're adding ML knowledge to deep infrastructure expertise. The first 2 months focus on Python and ML fundamentals. Months 3-4 on hands-on projects. Months 5-6 on job search and positioning. Networking specialists often land roles faster because they're rare in AI.

MLOps Engineer is the most natural fit - deploying, scaling, and monitoring ML systems. Inference Infrastructure Engineer focuses on serving models at low latency (your network optimization background shines here). Platform Engineer for ML builds the underlying infrastructure for ML teams. AI Systems Engineer works on distributed training and GPU cluster networking. These roles value your infrastructure expertise as much as ML knowledge.

AI infrastructure roles typically pay 30-50% more than equivalent network engineering positions. Senior MLOps Engineers earn $180K-$250K at major tech companies in 2026. Your rare combination of infrastructure depth and ML skills commands a premium. Many network engineers see significant salary increases within the first year of transitioning to AI-focused roles.

No. AI infrastructure roles prioritize practical skills over academic credentials. Your network engineering experience demonstrates you can handle complex systems. Companies hiring for MLOps and AI infrastructure specifically want people who understand production systems - that's you. A portfolio of projects showing you can deploy and optimize ML systems matters more than coursework.

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.

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.

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.