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AI/ML engineering

AI/ML developerswho monitorproduction, notjust deploy.

AI/ML development is more than calling the OpenAI API. Our developers build production AI systems — LLM applications, RAG pipelines, fine-tuned models, and inference APIs that are monitored, evaluated, and maintained. Real machine learning, not demos.

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Vetted senior talent

Flexible engagement models

Founder-direct delivery

rag_pipeline.py

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# services/rag_pipeline.py
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Chroma

def build_rag_chain(collection: str):
    vectorstore = Chroma(
        collection_name=collection,
        embedding_function=get_embeddings(),
    )
    retriever = vectorstore.as_retriever(
        search_kwargs={"k": 5}
    )
    return RetrievalQA.from_chain_type(
        llm=ChatOpenAI(model="gpt-4o"),
        retriever=retriever,
        return_source_documents=True,
    )
# RAG pipeline. Vector search. Source attribution. Production.

RAG

LLM / RAG · PyTorch / TensorFlow

LangChainProduction ML

EVAL

MLMLOps

LLM / RAGPyTorch / TensorFlowLangChainProduction ML

Why Entalogics

Why teams hire
AI/ML developers from us.

Our AI/ML developers work across the full stack — LLM integration with OpenAI and Anthropic, RAG systems with Pinecone and pgvector, agent frameworks with LangChain, and traditional ML with PyTorch. We focus on evaluation pipelines and MLOps so models stay reliable in production.

EXPERT BENCH01

Expert AI/ML developers, only.

Vetted for production AI — not notebook experiments. RAG architecture, embedding model selection, evaluation frameworks, hallucination detection, and real experience deploying AI features with monitoring and cost controls.

FAST HIRING02

Fast hiring process.

AI developers who call an API are everywhere. AI developers who build eval pipelines, handle retrieval chunking strategy, and deploy with cost monitoring — that pool is small. We keep them ready.

FLEXIBLE ENGAGEMENT03

Flexible engagement.

Dedicated developer for a long-term AI product. Part-time specialist for adding LLM features to an existing app. Hourly consultant for a RAG review or cost audit. You choose.

Hiring model

Three ways
to staff your build.

Most "AI developers" know how to call an API and stream a response. Production AI requires evaluation frameworks, observability, cost management, and engineering discipline so systems don't silently mislead users. Our developers treat training, evaluation, and deployment as one system — not three separate experiments.

M01160h / mo

Full-time hiring

A dedicated AI/ML developer embedded in your team full-time — building LLM integrations, RAG pipelines, fine-tuning workflows, and production inference APIs. Best for teams building AI as a core product capability.

M0280h / mo

Part-time hiring

20 hours per week of focused AI/ML work. Ideal for teams adding LLM features to an existing product, running prompt iterations, building eval datasets, or migrating between model providers.

M0340h blocks

Hourly hiring

Targeted AI/ML consulting — RAG architecture design, vector database selection, prompt review, cost analysis, or AI feasibility assessment. Scoped, delivered, done.

Engagement models

Who runs
the project.

Pick the engagement model that fits how your team runs AI/ML work — or talk to us and we'll suggest the right one.

Entalogics managed team

We own delivery end-to-end — AI architecture, model integration, evaluation pipelines, and production deployment. You review weekly and steer the product.

PM + senior AI/ML engineers on staff
We own the inference and eval pipeline
Model performance reports weekly

02MOST COMMON

Client managed team

Your engineering lead sets priorities. Our AI/ML developer joins your Slack, your GitHub, your standups. They build AI features, tune prompts — you direct the product.

Embedded in your Slack & GitHub
Reports into your engineering lead
Same hiring bar — you direct

Hybrid model

Your team handles product decisions and frontend. Our AI/ML specialist handles the intelligence layer — prompts, RAG, model serving, evaluation, and cost management.

Specialists from us, generalists from you
Shared standups + eval reviews
Clear DRI per AI feature

Stack & tooling

Technologies our
AI/ML developers use competently.

Full AI/ML stack from LLM integration to classical machine learning. Here's what our engineers work with daily.

Python

Pipelines, APIs, and model training scripts.

PyTorch

Deep learning training and fine-tuning.

TensorFlow

Production ML models and serving graphs.

scikit-learn

Classical ML for tabular and baseline models.

LangChain

RAG chains, agents, and tool orchestration.

LangGraph

Stateful multi-step agent workflows.

Related work

You might
also need.

Adjacent services we often ship alongside AI/ML work — same bench, same standard.

0101 — Hire Dedicated Developers 0202 — Web Development Services 0303 — UI/UX Design Services 0404 — Portfolio - Omni Brow Browser 0505 — Portfolio - Matchups Electron Browser 0606 — SaaS Development

COMMON QUESTIONS

Straight answers.

Six questions we get on every first call about hiring AI/ML developers. If yours isn't here, we'll cover it first.

LLM-powered features, RAG systems for knowledge bases, AI agents with tool use, recommendation engines, document processing pipelines, and traditional ML for prediction and anomaly detection. From API integration to custom model training.

Both. OpenAI and Anthropic for general LLM tasks. Open-source models via HuggingFace or Ollama when data privacy or cost control requires it. Fine-tuning when the base model needs domain adaptation. Matched to the problem, not the trend.

Retrieval-Augmented Generation connects an LLM to your private data so it answers using your information with source attribution — without fine-tuning. You need it when the LLM must reference your company's documents, knowledge base, or database accurately.

Evaluation pipelines with automated test suites. Hallucination detection. Human-in-the-loop for high-stakes outputs. A/B testing between model versions. Monitoring dashboards tracking accuracy, latency, and cost. No AI feature ships without an eval framework.

Yes. Ollama, vLLM, or ONNX Runtime on your infrastructure. No data sent to external APIs. For hybrid approaches, Azure OpenAI or AWS Bedrock where data stays within your cloud tenant.

Model selection matched to task complexity — expensive models for hard tasks, cheap models for simple ones. Query caching. Prompt optimisation to reduce tokens. Cost dashboards with alerts. Most AI cost problems come from using GPT-4o for every request.

Founder-direct

Ready to build
something amazing?

Discuss your AI/ML needs with Entalogics — quality delivery at fair pricing.

Get a quote

Async — scoping doc back in 48h

Schedule a call

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hello@entalogics.com

Email — replies within 24h

AI/ML developerswho monitorproduction, notjust deploy.