HIRE DEVELOPERS / AI/ML DEVELOPERS
Calling the OpenAI API is not AI/ML development — that's the easy 10%. Our AI/ML developers and machine learning engineers 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.
LLM integration with OpenAI and Anthropic, RAG systems with Pinecone and pgvector, agent frameworks with LangChain, NLP and traditional ML with PyTorch — the full stack our AI/ML developers and machine learning engineers work across. Evaluation pipelines and MLOps are the focus, so models stay reliable in production, not just in a demo.
RAG architecture, embedding model selection, evaluation frameworks, hallucination detection, and real experience deploying AI features with monitoring and cost controls — vetted for production AI. Not notebook experiments dressed up as a portfolio.
AI developers who call an API are everywhere. AI developers who build eval pipelines, handle retrieval chunking strategy, and deploy with cost monitoring are a small pool. We keep them ready.
A dedicated developer for a long-term AI product. A part-time specialist for adding LLM features to an existing app. An hourly consultant for a RAG review, an AI consulting session, or a cost audit. Sized to fit.
Calling an API and streaming a response is what most "AI developers" know how to do. Production AI requires evaluation frameworks, observability, cost management, and engineering discipline so systems don't silently mislead users. Our AI/ML developers treat training, evaluation, and deployment as one system, not three separate experiments.
One AI/ML developer, embedded full-time, building LLM integrations, RAG pipelines, fine-tuning workflows, and production inference APIs. Right for teams building AI as a core product capability.
Twenty hours a week of focused AI/ML work — enough for adding LLM features to an existing product, running prompt iterations, building eval datasets, or migrating between model providers.
A RAG architecture design, a vector database selection, a prompt review, a cost analysis, or an AI feasibility assessment — targeted AI/ML consulting, scoped and delivered.
Match this to how your team already runs AI/ML work, or tell us and we'll suggest one.
AI architecture, model integration, evaluation pipelines, and production deployment — all ours end to end. You review weekly and steer the product.
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.
Product decisions and frontend stay with your team. Prompts, RAG, model serving, evaluation, and cost management become ours — the intelligence layer.
Full AI/ML stack from LLM integration to classical machine learning — what our engineers actually work with, daily.
Pipelines, APIs, and model training scripts.
Deep learning training and fine-tuning.
Production ML models and serving graphs.
Classical ML for tabular and baseline models.
RAG chains, agents, and tool orchestration.
Stateful multi-step agent workflows.
Adjacent services we often ship alongside AI/ML work — same bench, same standard.
Six questions we get on every first call about hiring AI/ML developers. If yours isn't here, we'll cover it first.
Discuss your AI/ML needs with Entalogics — quality delivery at fair pricing.
