UB CS MS (Fall 2026 start): which courses for AI engineer, FDE, and SWE roles?

I'm a first-semester MS CS student at UB. I'm targeting three role types: AI engineer (applied AI, LLM engineering), forward deployed engineer (FDE), and general SWE. The program is 30 credits over three semesters, so every slot counts.

Background: B.Tech in CSE (AI/ML), currently an RA in a computational neuroscience lab. Projects include a multi-agent RAG system (LangGraph, FastAPI, vector DBs), LoRA fine-tuning, and a BPE tokenizer built from scratch.

Already fixed:

• Fall 2026 (in progress): Operating Systems, Algorithms, Intro to Machine Learning, Computer Security
• Deep Learning (as my capstone, final semester) and Reinforcement Learning

Choosing the remaining four slots from: Distributed Systems, Database Systems, Information Retrieval, Agentic AI Engineering (special topics, if offered), High Performance Computing I

My current plan:

• Spring 2027: Distributed Systems, Database Systems, Agentic AI (if offered)
• Fall 2027: Deep Learning, Reinforcement Learning, Information Retrieval

Questions:

1. Which of these help most across all three role types? Do systems courses (Distributed Systems, Database Systems) beat extra ML electives?
2. For FDE roles, do courses matter at all, or is it mostly projects and customer-facing skills?
3. Is Distributed Systems (Go) plus Database Systems (C++ query engine) in one semester realistic alongside a 20 hr/week RA job?
4. Is Information Retrieval worth it for LLM and RAG work, or is HPC I a better pick?
5. Has anyone taken Reinforcement Learning in their first year? The catalog mentions "Master's Year 2" and I can't tell if that's an enrollment restriction.
6. Would you change anything in this plan, and which courses actually came up in your interviews?

Thanks in advance.