Started my career as a C# developer, then moved into application design and architecture, followed by Azure, and now I’m mainly working in AWS and DevOps.
I want to transition into becoming a Senior Applied AI Engineer. The kind of role I’m interested in is designing and architecting AI-enabled applications, working with LLMs, agentic workflows, AI integrations, orchestration, automation, and possibly MLOps.
What I’m not really interested in is going deep into the maths, data titlescience, or traditional ML research side of things. Most roadmaps I’ve seen seem heavily focused on statistics, model training, and data science, which doesn’t feel aligned with the kind of AI engineering work I want to do.
I’m more interested in:
- AI application architecture
- LLM integrations
- Agentic systems and workflows
- AI platforms and infrastructure
- RAG systems
- MLOps and deployment
- Cloud-native AI systems
- AI security, governance, and observability
Given my background in software engineering, cloud, and DevOps, is there a roadmap specifically for Applied AI Engineering?
Would love advice from people already working in this space, especially on:
- What skills actually matter
- What to ignore
- Good projects to build
- Certifications or courses worth doing
- Whether deep ML knowledge is really necessary for senior roles
submitted by /u/argumentnull
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Originally published at reddit.com. Curated by AI Maestro.
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