Nine layers, one path to production.

From the foundations under a model to fine-tuning, serving, and running it at scale. Pick a layer and read it whole: its competencies, the concepts behind them, the technologies that implement them, and the production practice that separates a shipped system from a demo. 252 items, each with a note, and most with a written guide and its sources.

of applicants join the team
Top 3%
of the team has shipped to production
100%
production AI systems shipped
200+
from foundations to scale
9 layers

In short

Bigcircle builds its team against this roadmap, nine layers from foundations through fine-tuning, serving, and production. Every engineer on the team has shipped at least one production system, and each layer names the production practice that separates a shipped system from a demo. When a prototype stalls before production, production AI engineering applies those practices to your system, and you own the code.

Key facts

Definition

Bigcircle's AI engineer roadmap is nine layers an engineer on the Bigcircle team is expected to have shipped, from the foundations under a model through retrieval, agents, evaluation, fine-tuning, serving, and production.

How Bigcircle does it

  1. Bigcircle reads a layer as competencies, then concepts, technologies, and the production practice that keeps a system honest under real traffic, cost, and adversarial input.
  2. Bigcircle hires every engineer on its team through five vetting stages, starting from a system they already built, then a break Bigcircle watches them fix.

Comparison

A syllabus that stops once a model API returns text leaves the failure modes for later, while Bigcircle's roadmap carries cite-or-abstain retrieval, a hard cap on agent loops, and checks that can stop an action before it reaches your systems.

Start here

Stage 01

Foundations

The engineering bedrock under every AI system. Before the model, the fundamentals that keep it honest in production.

Programming and asyncData handlingML fundamentalsEngineering hygiene

Read stage 01

Stage 02

LLM applications

Turning a raw model into a dependable product surface, with output you can actually build on.

Model APIsPrompt engineeringStructured outputTool and function calling

Read stage 02

Grounding and retrieval

Stage 03

Retrieval and RAG

Grounding answers in real data, with citations, so the system says what is true rather than what is plausible.

Embeddings and chunkingVector storesRetrieval qualityRAG patterns

Read stage 03

Stage 04

AI agents

Systems that plan and act across real tools, check their own work, and recover when a step goes wrong.

Planning and controlTool useMemory and stateOrchestration

Read stage 04

Beyond text

Stage 05

Multimodal and voice

Vision, generation, and real-time voice, built to stay fast and grounded on a live call.

Vision and documentsImage generationSpeechRealtime voice

Read stage 05

Measure and improve

Stage 06

Evaluation and error analysis

The core production discipline. Not generic metrics, but looking at your own data, naming failures, and measuring the things that actually break.

Error analysisCustom evaluatorsLLM-as-judgeOffline CI and online monitoringTracing

Read stage 06

Make the model yours

Stage 07

Fine-tuning and adaptation

When prompting and retrieval run out, reshape the model itself. Done right, a small fine-tune can match a frontier model on your task at a fraction of the cost.

When to adaptSupervised fine-tuningPreference optimizationDistillation

Read stage 07

The systems layer

Stage 08

Serving and inference

What it takes to run a model yourself, fast and affordably. The two-phase nature of inference governs your real cost and tail latency, and it is where most teams have no depth at all.

Self-host vs APIInference enginesThroughput and latencyCompression and scale

Read stage 08

Ship and operate

Stage 09

Production and LLMOps

Running it for real, inside a latency and cost budget, observable, defended against hostile input, and reliable enough that customers feel it.

DeploymentObservability and LLMOpsGuardrails and securityCost and FinOpsReliability

Read stage 09

Nine layers deep, and every one of them already shipped.

Not one engineer here
who hasn't shipped.

The roadmap is the ground our team already covers. Here is some of the production AI behind it, real builds in real use, the kind we can walk you through live.

See all shipped work

Common questions

  • What is Bigcircle's AI engineer roadmap?

    Bigcircle publishes nine layers, from foundations through LLM applications, retrieval, agents, multimodal and voice, evaluation, fine-tuning, serving, and production. Open a stage for the competencies, the concepts, the technologies, and the production practice on that layer.

  • How does Bigcircle build its team against this roadmap?

    The top 3% of applicants make it through, and 100% of the engineers on the Bigcircle team have shipped to production. Vetting starts from a system they already built, then breaks something and watches the fix. The engineers who pass stay on one product at a time rather than being split across accounts.

  • Where should a learning path start?

    Start at foundations, the engineering bedrock under every AI system, before the model. Bigcircle then expects LLM applications, retrieval, and agents, and the later stages cover evaluation, fine-tuning, serving, and production.

  • Has Bigcircle shipped every layer?

    Nine layers deep, and every one of them already shipped. The roadmap is the ground the Bigcircle team already covers, and the case studies linked from the page are production AI Bigcircle can walk through live.

The team that builds your system works this way.

Every layer above is work our team has shipped to production. Tell us what you are building and we will scope it: a fixed-scope build, a prototype taken to production, or a programme we run.

Scope a buildSee the work