LLM Orchestration — Constrained System
7 hours ago
Job description
The problemA quant is awake at 2am.
He types:
"Give me 1s and 0s for when the Big 7 tech stocks are in regime 1 but the rest of the NDX 100 is in regime 0."
When are the generals marching but the army isn't following?
A strategist is reading on a Sunday morning.
She types:
"Combine Gold, TLT, and VIX into a single fear index. Give me 1s and 0s."
One number that tells her when all the fear assets agree.
A desk doesn't trust short timeframes.
They type:
"BTC on 5-minute bars says 1. BTC on daily bars says 0. Flag every moment they disagree."
When is the short-term lying about the long-term?
Someone gets noisy output.
They type:
"Too noisy. Run it again until stable."
Recursive self-application until the signal stops changing.
This is not a speculative build. The underlying system is complete and deployed.
What exists
— A Python library of deterministic functions. Frozen. Verified. Done.
— The functions share a uniform interface. They require no parameters. They encapsulate their own alignment, warmup, and resampling. You call them, you get a result. Same input, same output, every time.
— The math is not your problem. The math is finished.
What you're building
— An orchestration layer between natural language and this library.
— Parse intent. Route to functions. Compose outputs. Evaluate stability. Return results.
— Not a chatbot. Query in, structured result out.
Technical shape
— Python library of deterministic functions (complete)
— LLM layer:
intent parsing → structured function invocation
— No fine-tuning. No learning loop. No memory.
— Stateless execution — each query independent
— Outputs must remain invariant
Core challenges:
— Mapping ambiguous language to exact function calls
— Composing multiple function outputs into a single result
— Detecting and enforcing output stability (convergence)
— Rejecting any intent that cannot map to the frozen library
Definition of done:
— Natural language queries reliably map to valid function compositions
— Invalid or ambiguous queries are explicitly rejected
— Repeated execution of the same query yields identical results
— Multi-step compositions converge to a stable output when required
What makes this different
— Most LLM orchestration rewards flexibility. This one rewards restraint.
— The system works because of what it doesn't do. The functions don't learn. The interface doesn't change. The architecture doesn't grow.
— Frozen means verified. Verified means attempts to improve it break it.
The real risk
Most systems fail by accumulation. This one fails the same way.
Every addition breaks it:
— Memory? Breaks statelessness.
— Learning? Breaks verification.
— Caching? Introduces hidden state.
— Flexibility? Breaks the constraint.
The architecture survives because someone removes what engineers want to add.
I'm looking for someone who knows when to say no.
The filter
No portfolio. No past work. Answer these three questions.
Question 1:
You're building an orchestration layer for a library of deterministic functions. A senior engineer proposes adding a caching layer to speed up repeated queries.
What is your first question before responding?
Question 2:
A user requests a feature that would require the underlying functions to "learn" from feedback.
The functions cannot be changed.
How do you respond?
Question 3:
You've been on the project three months. You realize a simplification that would eliminate an entire component — but it means admitting the component was unnecessary.
What do you do?
What I'm reading for
Question 1:
Whether you ask "why" before "how."
Question 2:
Whether you protect constraints or engineer around them.
Question 3:
Whether you protect the system or protect your ego.
Compensation
Project-based.
Senior-level budget.
If there's strong alignment, this extends into a longer-term role.
Contract duration of 1 to 3 months.
Mandatory skills:
Large Language Model, Prompt Engineering, AI Model Integration, API Integration, ChatGPT API Integration, Python
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