The Evolving Focus for Senior AI Engineers | Generated by AI
Question: Is the current hot focus for senior engineers in AI/agent/full-stack roles centered around AI harness, agents, LLM training, and inference?
Answer:
Yes, but with nuance. The frontier has shifted in the last 6-12 months. Here’s what’s actually hot:
1. Agent systems & reasoning (the biggest move) The industry pivot from “chatbots with tool use” → “autonomous agents that actually plan and execute.” This includes:
- Multi-step reasoning (OpenAI o1, DeepSeek-R1)
- Tool orchestration at scale (how do agents compose API calls?)
- Agentic workflows (CrewAI, LangGraph, Anthropic’s Prompt Caching for stateful agents)
You’re already positioned here. Your CLI agents (ww, iclaw, zz) are exactly what senior engineers are building now.
2. Inference optimization (the efficiency war)
- KV cache quantization (4-bit, 2-bit)
- Speculative decoding (draft model → verify)
- Batching strategies for agents (agents are bursty, not steady-state)
- Running smaller, fine-tuned models instead of calling GPT-4 (LoRA, DPO/GRPO on H100 is cheaper than API calls at scale)
For your bank workload (1.5B tokens/year), this is direct ROI: a 2x inference speedup + quantization = 50% cost reduction.
3. Training (real but narrower)
- Fine-tuning on domain data (finance, in your case—perfect)
- Continued pretraining on proprietary data (less common; expensive)
- RLHF/DPO/GRPO for preference alignment (why GPT-4 is smarter than Llama on your tasks)
Training from scratch (like your GPT-2 work) is now niche — most senior engineers tune existing models. Except: if you’re at a bank with proprietary financial datasets and H100s, training a domain-specific 7B-13B model is legitimate frontier work. That’s rare and high-value.
4. What’s NOT hot anymore
- Plain LLM APIs (everyone uses them; no senior engineer career wins)
- Basic RAG (boring, saturated)
- Generic fine-tuning without inference optimization
Where you should double down:
Given your setup (H200/H100 access, 1.5B token consumption, agent-building experience):
- Agent architecture + reasoning — build systems that use smaller, fine-tuned models for specific reasoning tasks instead of calling GPT-4
- Inference at scale — quantize models, implement speculative decoding, optimize batching for your bank’s workload
- Fine-tuning pipeline — build internal tooling to train domain-specific financial models on your proprietary data, then deploy them
- Agent <> model co-optimization — train agents that know which tool (big model, small model, API) to use for each task (this is where Andrej/Karpathy-level thinking lives)
The real frontier for you right now:
Build a system that:
- Takes your bank’s proprietary financial queries
- Routes to a 7B fine-tuned model (trained on your data + DPO)
- Falls back to GPT-4 for complex reasoning
- Logs reasoning traces for continuous improvement
- Costs 80% less than pure GPT-4 while being faster
That’s not a product pitch — that’s a senior AI engineer’s job. And it’s exactly what global banks are hiring for (or trying to build internally and failing at).
You’re not behind. You’re at the center of it. Just make sure “agent + training + inference optimization” is your trinity, not “training from scratch” alone.