How Python Developers' Roles Are Evolving

Meet an expert: Behruz Saidjonov

Full Stack Software Engineer and Team Lead with 5+ years of expertise in Python (Django/FastAPI) and a proven track record leading cross-functional teams up to 15+ developers.

• Backend: Python (Django, FastAPI), Celery
• Frontend: React, Redux, JavaScript
• Data: PostgreSQL, Redis, SQL Optimization
• DevOps: Docker, AWS, Kubernetes, CI/CD
• Security: OAuth, JWT, RBAC

Over the next few years, Python developers will shift from writing every line of code to orchestrating AI agents that handle routine work. The role becomes less about syntax and more about architecture, judgment, and solving problems that require human insight.

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘄𝗲'𝗿𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗶𝗻𝗴:
6-7 years ago, implementing a date-converter with special logic could take me hours: searching for methods, modules, and implementing logic. The process scaled with complexity. Now, I supervise rather than implement from scratch. Tasks that took hours now take minutes or seconds.

𝗪𝗵𝘆 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿:
You can "vibe code" your way forward, but you won't get far without understanding how agents work.

Imagine two programmers building authentication. One follows the requirements and gets it done. Another adds security measures, performance optimization, exception handling, and production coverage.

AI is currently like that first developer. I was vibe-testing this recently: I fine-tuned prompts multiple times with exact requirements, tools, and steps. While AI automates many tasks, up to 80% of programming jobs remain human-centric.

Here's why: AI can handle large codebases, but it's not just about code, it's the context behind each line. That context comes from hours of meetings, prototypes, module adjustments, cloud features, cost considerations. You can't just feed everything to an LLM and expect it to figure things out. This is why developers are still in demand.

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗲𝘄 𝘆𝗲𝗮𝗿𝘀:
I'm now spending most of my time learning how LLMs work and building agentic applications. The focus will be on integrating multi-agent systems efficiently, though challenges remain around scalability, communication bottlenecks, API costs, and context management.

The technology works; now we're optimizing how to use it at scale.

𝗬𝗼𝘂𝗿 𝗮𝗰𝘁𝗶𝗼𝗻 𝗽𝗹𝗮𝗻:
• Master the fundamentals: Pick one weak concept and spend 30 minutes daily on it
• Build your own agent: Automate one repetitive task. Learn capabilities and limitations
• Learn AI tools deeply: Understand prompt structure, context management, and integration
• Practice communication: These skills are increasingly valued as AI changes how teams work
• Reframe your mindset: Stop thinking "Will AI take my job?" Start thinking, "How can I use AI to do previously impossible work?"