LLM Developers

Hands-on engineering for RAG, fine-tuning, safety, evals, and production APIs

Need hands-on LLM developers?

Oodles AI provides experienced LLM developers who design, build, and operate production-grade language model systems with evaluations, observability, security guardrails, and cost controls.

LLM developers who ship reliable, governed AI features

Work with Oodles AI’s LLM developers to build production-ready applications using large language models. Our engineers specialize in retrieval-augmented generation (RAG), prompt engineering, fine-tuning, safety layers, and scalable APIs aligned with enterprise data and governance requirements.

LLM developers working on production AI

Build, align, and ship with the right guardrails

Our LLM developers manage the full lifecycle of language model systems using Python, FastAPI, LangChain, vector databases, and cloud infrastructure. We deliver RAG pipelines, prompt frameworks, fine-tuned models, safety filters, monitoring dashboards, and operational runbooks to keep LLM features stable in production.

What we deliver

  • • Retrieval-augmented generation (RAG) using vector databases and embeddings
  • • Prompt engineering frameworks with safety and policy enforcement
  • • Fine-tuning and parameter-efficient tuning (LoRA, adapters)
  • • Evaluation pipelines, red-teaming, and regression testing
  • • Production APIs with logging, monitoring, and cost optimization

Why it works

  • • Architecture-first approach to model selection and latency optimization
  • • High-quality data pipelines with chunking, metadata, and retrieval tuning
  • • Built-in safety: PII masking, content filters, and jailbreak resistance
  • • Cost efficiency through caching, batching, and token budgeting
  • • Reliability ensured by automated evals and performance monitoring

Where our LLM developers plug in

Targeted engineering support across product, data, and platform teams.

RAG apps

Search, summarization, and assistant applications using vector search, citations, and fallback logic.

Agent workflows

Multi-step agent systems with tool calling, orchestration, and guardrails for controlled execution.

Domain tuning

Fine-tuning and PEFT methods to align model outputs with domain, tone, and compliance requirements.

Safety & compliance

PII detection, output filtering, jailbreak testing, and audit-ready logging pipelines.

Product integrations

API-driven LLM features integrated into web, mobile, CRM, and internal platforms.

Evals & monitoring

Golden datasets, regression testing, drift detection, and dashboards for quality and cost visibility.

How our LLM developers deliver

A disciplined build-measure-iterate workflow used by Oodles AI to deliver secure, scalable, and production-ready LLM systems.

1

Architecture & model choice

Select the optimal LLM family, context window, and performance profile.

2

Data & retrieval setup

Set up embeddings, vector search, and retrieval logic to ground responses.

3

Safety & guardrails

Implement safety layers including PII masking, abuse filters, and policy checks.

4

Evals & tuning

Run evaluations, prompt tuning, and fine-tuning to improve accuracy.

5

Deploy & observe

Deploy APIs with monitoring, alerts, and cost controls for production use.

Request For Proposal

Sending message..

Need senior LLM developers? Let's talk