Hire AI / ML Developers

AI engineering changed shape in 2024-2026 — the work today is less "train a model from scratch" and more "ship a production LLM-powered application with evals, retrieval, agents, and a real cost model." Hire Programmers places senior engineers who have done both: classical ML pipelines and modern LLM-app engineering.

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Why companies hire AI / ML developers from Hire Programmers

LLM application engineering: prompt design, structured outputs, function calling, multi-turn agents

Retrieval-augmented generation (RAG): chunking, embeddings, vector stores (pgvector, Qdrant, Weaviate), reranking

Evals + observability: eval suites, golden datasets, LangSmith / LangFuse / Helicone telemetry

Model fine-tuning: PEFT / LoRA, full SFT, RLHF / DPO when the data justifies it

Classical ML where it is still the right answer: scikit-learn, XGBoost, ranking, time-series

MLOps: model registry, batch / streaming inference, GPU cost engineering, prompt caching

Frequently asked questions

Yes. We staff engineers who ship daily against the major LLM APIs (OpenAI, Anthropic, Google, open-weight models via vLLM / TGI), with strong evals discipline. We do not staff engineers who learned ML in 2018 and have not touched a transformer since.
Yes. Tool-using agents (function calling), planner + executor architectures, and multi-step workflows are common engagements. We will push back on "agent" framings that should actually be a deterministic pipeline — that conversation tends to save clients money.
Both. PEFT / LoRA fine-tuning on open-weight models for domain-specific tasks is in scope; full SFT and RLHF / DPO are available when the data and engagement size justify it. Most clients are better served by good RAG + prompt engineering before fine-tuning; we will say so when that is the case.
Yes. Prompt caching, model-routing (cheap models for easy queries, expensive models for hard ones), context-window discipline, and batch APIs are all in the toolkit. Token economics is a real engineering concern in 2026 and we treat it as one.

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Tell us about your AI / ML project and we will line up a matched engineer within days.

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