Retrospective XVII

Last year, I conducted a simple retrospective for 2024. Therefore, here is a retrospective for year 2025.

2025 Achievements

Document DevOps managed clusters provisioning setup E.g.: Azure AKS, AWS-EKS, GCP-GKE Present + report The Evolution of Software Deployment Cloud CI/CD setup theory to practical Consolidate pytest unit test framework features such as fixtures, factories, mocking, patching Transition Python package management from pip to poetry and finally to uv for blazing speed Introduction to MLOps theory learning to practical ML models and deployment implementation Extend GitLab CI/CD experience to upskill to GitHub Actions executing on localhost using ACT Resurrect PyBind knowledge on OpenAI Retro to Mac on CLion for Python to C/C++ examples Reverse Engineer RetroAchievements project open source code for Simpsons Trivia integration Note: Reverse Engineering and setup RetroAchievements project open source code is a big achievement!

2026 Objectives

Document Python package management move from pip to poetry to uv for new Python projects Continue MLOps integration to bridge Software Engineering to Machine Learning knowledge gap Register PyBind Python to C/C++ setup cross platform and explore debugging across languages Harness prior OpenAI Retro and RetroAchievements experience and apply to future AI projects!

AI Demand
According to 2025 analysis global AI and ML Engineer job openings numbering in the hundreds of thousands with employers increasingly willing to hire based on demonstrable skills rather than advanced degrees. One market study forecasts AI Engineering sector could grow from $17.4 billion in 2025 to $87.5 billion by 2030 implying a sustained growth rate for the next 5yrs.

Therefore, as a Professional Software Engineer how are some ways to transition to a more focused AI-role?

MLOps Engineer
By expanding traditional Software Development Life Cycle skills across DevOps as modern delivery practices have evolved one next step is the progression into MLOps: enabling the deployment monitoring and the mgt of machine learning models built by dedicated ML Engineers. This provides immediate value to the AI-driven teams while supporting ongoing machine learning expertise.

AI Engineer
An AI Engineer specializes in building integrating deploying and optimizing machine + deep learning systems bridging the gap between research models and production-ready software. A Software Engineer focuses on: application logic architecture performance and code quality whereas an AI Engineer works at the intersection of Software Engineering and Data Science.

Future
In 2024 we checked out OpenAI Gym and Retro to provide early exposure to AI-centric principles integrating Reinforcement Learning environments using Python and C/C++ to upskill AI concepts such as agent training environment design reproducibility and iterative experimentation.

In 2025 we checked out RetroAchievements ecosystem to further deepen these skills dealing with emulation internals memory inspection and state tracking all highly relevant to RL and model-driven control systems.

Together this experience forms connection between long-standard Professional Software Engineer expertise and the emerging demands of AI Engineering. Combining systems programming with complex environment instrumentation and practical RL-work aligns with one of the fastest-growing roles in the technology sector!