From IT Support to AI Engineer: A Career Path Framework

Jan 22, 2026•10 min readCareer Development

The Foundation Years: Building Core Technical Skills

My journey began in 2015 as a Technical Support Analyst in Brazil, where I learned the fundamental skills that would later prove invaluable in my transition to AI engineering. These weren't just ticket-resolving skills—they were the bedrock of technical problem-solving that no amount of AI theory can replace.

Critical skills developed during early support roles:

  • Systematic troubleshooting: Learning to break down complex problems into manageable components
  • Root cause analysis: Moving beyond symptoms to understand underlying issues
  • Technical communication: Translating complex concepts for different audiences
  • Cross-platform fluency: Working across Windows, Linux, and various enterprise systems
  • Process documentation: Creating clear, reproducible solutions for knowledge sharing

These foundational skills are often overlooked in AI career discussions, but they're absolutely critical. The best AI engineers I know aren't just those who understand neural networks—they're those who can deeply understand problems, communicate effectively, and build solutions that actually work in real-world constraints.

The Bridge Phase: Expanding Technical Horizons

After building a strong foundation in IT support, I began intentionally expanding my technical repertoire. This phase wasn't about abandoning support skills—it was about building upon them with adjacent competencies that would increase my impact and open new opportunities.

Strategic skill expansions that built the bridge:

  • Web development (2018-2019): Built end-to-end web systems with PHP, MySQL, and ExtJS
  • Data analysis (2019-2020): Performed data analysis and visualization to extract business insights
  • Automation focus: Built scripts to automate data-gathering and repetitive processes
  • Cloud exposure: Gained experience with AWS services and basic infrastructure concepts
  • Version control proficiency: Mastered Git workflows and collaborative development

The key insight here is that each expansion built logically upon the previous skills. Web development taught me about APIs and databases—concepts directly applicable to AI engineering. Data analysis built my comfort with statistics and patterns—foundational for machine learning. Automation work gave me practical experience in building tools that solve real problems.

The Transition Point: Applied AI in Support Context

The real inflection point came when I started applying AI concepts directly within my support engineering role at Coupa Pay. Rather than making a clean break from support to AI, I found a powerful middle ground: using AI to enhance and scale the impact of traditional support engineering.

Applied AI projects that facilitated the transition:

  • Intelligent ticket triage: Fine-tuned transformers for automatic classification and routing
  • Knowledge base automation: Systems that convert resolved tickets into draft KB articles
  • Slack AI skills: Purpose-built tools that automate repetitive support tasks
  • Predictive support: Using logs and metrics to anticipate issues before customer impact
  • AI-assisted documentation: Leveraging LLMs to improve technical writing efficiency

This approach had several advantages over a traditional career switch:

  • Immediate application of AI concepts to real business problems
  • Ability to measure impact and iterate quickly based on feedback
  • Continued leveraging of deep domain knowledge in support engineering
  • Lower risk transition with built-in fallback to existing expertise

Most importantly, this approach allowed me to build credibility in AI engineering not through certifications alone, but through tangible results: reduced response times, increased automation rates, and measurable time savings across the organization.

The AI Engineer Phase: Specialization and Depth

After establishing a track record of successful AI applications in support contexts, I began to deepen my technical expertise in specific AI domains. This phase involved more formal study, experimentation with advanced techniques, and contribution to the broader AI engineering community.

Areas of specialization pursued:

  • Natural Language Processing: Advanced techniques for text classification, summarization, and generation
  • Prompt engineering: Systematic approaches to optimizing LLM outputs for specific use cases
  • Fine-tuning methodologies: When and how to adapt pre-trained models to specific domains
  • MLOps fundamentals: Model versioning, monitoring, and maintenance in production
  • AI safety and ethics: Responsible development practices for impactful AI systems

The transition from applied AI to AI engineering isn't about abandoning practicality— it's about gaining the depth to innovate rather than just implement. With stronger theoretical foundations, I could now:

  • Design custom solutions rather than relying solely on existing tools
  • Contribute to open-source AI projects and community knowledge
  • Evaluate emerging technologies for potential adoption
  • Architect scalable AI systems rather than just point solutions

Practical Framework for Your Transition

Based on my experience and observations of successful transitions in the industry, here's a practical framework that technical professionals can adapt to their own circumstances:

Phase 1: Strengthen Your Foundation (Months 1-3)

  • Document your current technical strengths and problem-solving methodologies
  • Identify 2-3 repetitive tasks in your role that could benefit from automation
  • Begin learning basic scripting (Python/PowerShell) to automate these tasks
  • Study fundamental concepts: algorithms, data structures, and basic statistics

Phase 2: Build Applied AI Projects (Months 4-6)

  • Apply your automation skills to solve one documented pain point
  • Experiment with pre-trained models for text classification or summarization
  • Build a simple tool that uses AI to enhance your existing work
  • Measure and document the impact of your solution

Phase 3: Deepen Technical Expertise (Months 7-9)

  • Study specific AI domains relevant to your interests (NLP, computer vision, etc.)
  • Learn about model training, fine-tuning, and evaluation techniques
  • Experiment with open-source tools and frameworks in your chosen domain
  • Contribute to small open-source projects or build portfolio pieces

Phase 4: Specialize and Share (Months 10-12)

  • Focus on a specific AI application area that aligns with your background
  • Build a substantial project that demonstrates end-to-end competence
  • Share your knowledge through blog posts, talks, or open-source contributions
  • Begin targeting roles that value both your domain expertise and AI skills

The journey from IT support to AI engineering isn't about leaving your past behind—it's about building upon it. Every troubleshooting session, every difficult customer conversation, and every process you've improved has given you valuable skills that AI engineering desperately needs. Your path may look different from mine, but the fundamental principle remains: leverage your unique background to build AI solutions that solve real problems in meaningful ways.