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Roast my 3-year roadmap: Pivoting from Python/BaaS to AI Infrastructure & Go (Graduating 2029) [D]
I'm a B.Tech student in India graduating in mid-2029. Currently, I know Python, SQL, Docker, basic prompt engineering, and I've built a few LLM apps using BaaS like Supabase/Firebase. I’m running all this on an Intel i5 13th Gen laptop with an RTX 5050 (8GB VRAM). The Pivot: I originally wanted to be a generic ML Engineer, but I've realized the entry-level market for Jupyter Notebook/Python scripters is insanely saturated. Plus, most companies are just calling OpenAI APIs anyway. I want to pivot to the high-performance stuff: AI Backend / Distributed Systems Engineering. Instead of tuning the model, I want to be the guy building the infrastructure that serves the model to 10,000 users without the servers melting. My ultimate goal is top-tier Indian product companies (Zepto, Cred, etc.) or US remote startups. My Proposed Roadmap: I am dropping the "easy" BaaS tools and going low-level. Here is my plan for the next couple of years: The Language Shift: Dropping Python for backend, going all-in on Go (Golang) to master concurrency and memory pointers. The Database Shift: Moving away from Supabase to raw PostgreSQL (via Docker), eventually learning to scale it, plus Redis for caching. The Hardware/AI Constraint: Running models locally on my RTX 5050 using Ollama/vLLM. Learning how to deal with VRAM limits using quantization, PagedAttention, etc., before eventually moving to cloud AWS/GCP. The Distributed Scale: Learning Kafka/RabbitMQ, Vector Databases (Milvus/Qdrant) at scale, and eventually wrapping it all in Kubernetes. Basically, I want to build custom, high-throughput AI backends from scratch. My Questions for the Seniors: Is this roadmap actually viable for a 2029 grad, or is the learning curve for Distributed Systems + AI Infrastructure too brutal to do alongside a college degree? Is betting heavily on Go the right move for this specific AI systems niche? What blind spots am I missing here? Roast my plan. Be brutally honest! submitted by /u/SinkClassic4450 [link] [Kommentare] reddit.com · reddit.com ↗
I'm a B.Tech student in India graduating in mid-2029. Currently, I know Python, SQL, Docker, basic prompt engineering, and I've built a few LLM apps using BaaS like Supabase/Firebase. I’m running all this on an Intel i5 13th Gen laptop with an RTX 5050 (8GB VRAM). The Pivot: I originally wanted to be a generic ML Engineer, but I've realized the entry-level market for Jupyter Notebook/Python scripters is insanely saturated. Plus, most companies are just calling OpenAI APIs anyway. I want to pivot to the high-performance stuff: AI Backend / Distributed Systems Engineering. Instead of tuning the model, I want to be the guy building the infrastructure that serves the model to 10,000 users without the servers melting. My ultimate goal is top-tier Indian product companies (Zepto, Cred, etc.) or US remote startups. My Proposed Roadmap: I am dropping the "easy" BaaS tools and going low-level. Here is my plan for the next couple of years: The Language Shift: Dropping Python for backend, going all-in on Go (Golang) to master concurrency and memory pointers. The Database Shift: Moving away from Supabase to raw PostgreSQL (via Docker), eventually learning to scale it, plus Redis for caching. The Hardware/AI Constraint: Running models locally on my RTX 5050 using Ollama/vLLM. Learning how to deal with VRAM limits using quantization, PagedAttention, etc., before eventually moving to cloud AWS/GCP. The Distributed Scale: Learning Kafka/RabbitMQ, Vector Databases (Milvus/Qdrant) at scale, and eventually wrapping it all in Kubernetes. Basically, I want to build custom, high-throughput AI backends from scratch. My Questions for the Seniors: Is this roadmap actually viable for a 2029 grad, or is the learning curve for Distributed Systems + AI Infrastructure too brutal to do alongside a college degree? Is betting heavily on Go the right move for this specific AI systems niche? What blind spots am I missing here? Roast my plan. Be brutally honest! submitted by /u/SinkClassic4450 [link] [Kommentare]
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