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For US, UK and EU teams building real AI products: LLM agents, RAG systems, fine-tuned models, voice AI, computer vision, custom MLOps pipelines. Senior engineers with production AI experience (not "I built a wrapper around ChatGPT API" pretenders), $80 to $180 per hour fully-loaded, EST/CST overlap, EU jurisdiction.
Trusted by teams across Europe
Most "AI engineers" on freelance marketplaces have 6 months of LangChain experience and built tutorial RAG demos. Our AI/ML engineers have 3+ years in production AI: shipped LLM agents serving 100K+ users, built RAG over enterprise document corpora, fine-tuned models on domain data, deployed inference at scale (vLLM, Triton, TensorRT). They know when to use OpenAI vs Anthropic vs open-source, when to fine-tune vs RAG vs prompt-engineer, when to use vector search vs hybrid search. The difference between "AI works in demo" and "AI works in production" is 10x development cost downstream. We staff for the second.
| Option | AI specialty depth | Cost (per dev/month) | Production AI experience | Risk |
|---|---|---|---|---|
| Hauer Power AI/ML team | High | $13k to $30k | 3+ years production AI, vetted on real codebases | Low — replaceable in 5 days, no lock-in |
| AI freelance marketplaces | Variable | $8k to $25k | Wide range, mostly junior, hard to verify | High — junior + AI hype = bad combo |
| US AI specialist firms (Anyscale, Modal) | 4 to 9 months to fill | $25k to $50k | Excellent, US-based premium | Low risk, very high cost |
| In-house AI hires | 4 to 9 months to fill | $28k to $55k Y1 | Excellent if you can hire | High — AI talent supply < |
| In-house hiring | 10 to 16 weeks | $11k to $20k Y1 fully-loaded | Whatever your recruiter does | 3+ months lost to recruitment, then onboarding |
| Upwork / Fiverr | 1 to 3 days | $2k to $9k | None, you screen | High — no SLA, no replacement, no IP protection |
Honest take: if you have $1M+ AI budget and want all-US team, hire from Anthropic/OpenAI alumni networks. We win when you need senior AI engineers but cannot pay $400K base + RSU, want EU GDPR/AI Act compliance, and need EST/CST overlap with retention. See nearshore cost analysis.
Pattern across recent AI engagements: B2B SaaS adding AI features, Series A fintechs with RAG over docs, healthcare with HIPAA-compliant inference, voice agents for customer support, fine-tuning for vertical-specific tasks.
Tell us what you need →Critical sprint mid-flight, knowledge transfer needed, in-house replacement is 90+ days away. We staff a senior backend engineer in 7 days for 30 to 90 day knowledge-transfer engagement, optionally permanent.
2 to 3 senior React engineers, 60 to 90 day surge to unblock product launch. Pair with your tech lead, ship features, hand off cleanly. No agency overhead, just engineering capacity on tap.
Internal recruiting will take 90 to 120 days. Investors expect velocity now. We staff a full nearshore squad in 7 days, while your in-house recruiter ramps the permanent team in parallel. Bridge the gap.
Code is in disarray, communication broken, deadlines slipping. We do a 5-day code audit, propose stabilization plan, then staff 2 to 4 engineers to take over. Existing engagement transitions cleanly within 30 days.
Compliance audit forced a hard deadline. Specialized engineers (security, infra, audit-trail logging) needed for 60 to 90 days. We have bench engineers with prior SOC 2 Type 2, HIPAA and GDPR audit experience. Day 7: PR review for compliance gaps.
Building agent systems with LangChain or LangGraph, multi-step reasoning, tool calling, function calling. Production-grade error handling, retries, observability, cost control. Not toy demos.
Vector databases (Pinecone, Weaviate, Qdrant, pgvector), hybrid search, re-ranking, chunking strategies, eval harnesses. Built RAG over PDFs, support tickets, codebases, regulatory docs.
LoRA, QLoRA, full fine-tuning on Llama, Mistral, Qwen, Phi. PEFT methods, RLHF/DPO/PPO when appropriate. Domain adaptation: legal, medical, financial, technical.
STT (Whisper, Deepgram), TTS (ElevenLabs, OpenAI), VAD, WebRTC streaming. Sub-500ms latency for real-time conversations. Built phone agents, customer support agents, sales agents.
CLIP, BLIP, GroundingDINO, SAM. Document understanding (OCR + VLM), receipt parsing, X-ray analysis, satellite imagery. Multimodal RAG with image+text retrieval.
vLLM, TGI, Triton for serving. Kubernetes, Ray for orchestration. Model registry, eval pipelines, A/B testing, drift detection. Cost monitoring (per query, per user, per feature).
LangSmith, LangFuse, Helicone, custom eval harnesses. Golden datasets, regression tests, hallucination detection, factuality scoring. You measure improvement, not vibes.
EU AI Act compliance, GDPR-compliant inference (no data retention), HIPAA-eligible AI deployments, SOC 2 audit support, on-prem inference for regulated industries. Self-hosted Llama for clients who cannot use OpenAI.
AI engineers command 1.5 to 2x premium over standard senior dev rates due to talent scarcity. Time and materials, monthly invoicing, no minimum commitment past 30-day notice. AI engineers from Anthropic/OpenAI alumni networks in US start at $250-$400/hr; Polish nearshore senior AI: $80-180/hr.
1 senior AI engineer (3+ years production AI: LangChain, RAG, fine-tuning), full-time. Right for: building first AI feature, MVP for AI product, technical due diligence support, post-funding AI surge.
2 senior AI engineers (LLM/agent specialist + RAG/embedding specialist) + 1 ML ops (Kubernetes, vLLM, Triton, monitoring). Right for: shipping production AI feature in 90 days, replacing demo-grade AI prototype, building AI agent platform.
2 senior AI engineers, 1 ML ops, 1 data engineer, 1 backend full-stack, 0.5 tech lead. Building dedicated AI product line: LLM platform, agent infrastructure, fine-tuning pipeline, eval harness, production observability.
What is the AI use case (RAG, agent, fine-tune, voice, vision). What is the data (size, format, sensitivity). What is the latency/cost/accuracy budget. What does success look like (metrics, not vibes). We propose engineer profile and rough scope within 48 hours.
2 to 3 candidates with: prior production AI work samples (with NDA carve-outs as needed), GitHub of AI projects, blog posts on AI engineering, prior client references in similar AI domain. Standard "looks good in CV" filter is not enough for AI work.
Your tech lead does 90-minute interview: AI system design, prompt engineering deep dive, hands-on RAG or agent debugging, eval methodology discussion. Decision within 24 hours after.
Standard MSA plus AI-specific addenda: data access scope, model output ownership, training data IP, no-train clauses for OpenAI/Anthropic API usage, EU AI Act risk classification. We have these templated.
AI engineer sets up dev environment, accesses data, runs initial eval baseline on existing AI system (if any). Week 1 deliverable: documented baseline + 3 hypothesis-driven improvements with measurable success criteria. Then iteration.
Every AI engineer in our bench has shipped at least one production AI system serving real users (not tutorial demos). Median experience: 3.5 years on AI specifically, 7+ years total. We verify with prior client work samples (NDA carve-outs as needed), AI portfolio (open source, blog posts, talks), and reference calls.
We say no to 92% of self-described "AI engineer" applicants. Most failed candidates: built tutorial RAG, never deployed inference at scale, no eval methodology, prompt-engineering only. Production AI is a different skill.
See full case studyAI is the easiest skill to fake on a CV. We require: (1) prior production AI deployment with measurable metrics (not "I built a RAG demo"), (2) work sample review under NDA, (3) live AI system design interview, (4) eval methodology discussion. We reject 92% of applicants. Most fail step 1 alone.
Senior AI engineer (3+ years production AI): $80 to $180 per hour ($13 to $30k per month at 40 hrs/week). ML ops: $70 to $140 per hour. Premium over standard senior dev rates due to talent scarcity. US equivalent: $250 to $400 per hour for AI specialists from Anthropic/OpenAI alumni networks.
LLMs: OpenAI, Anthropic, Cohere, Mistral, open-source (Llama, Qwen, Phi). Frameworks: LangChain, LangGraph, LlamaIndex, DSPy, Haystack. Vector DBs: Pinecone, Weaviate, Qdrant, pgvector, Chroma, Milvus. Fine-tuning: PEFT (LoRA, QLoRA), full SFT, DPO, RLHF. Inference: vLLM, TGI, Triton, TensorRT, Ollama. Voice: Whisper, ElevenLabs, Deepgram, Cartesia, OpenAI Realtime. Vision: CLIP, BLIP, SAM, GroundingDINO. MLOps: Kubernetes, Ray, MLflow, W&B, LangSmith.
Eval and observability: if not the right fit by day 30, we replace within 5 business days at no cost. You only pay for hours actually worked. In practice, replacement happens in less than 5 percent of engagements (mostly due to client priorities changing, not engineer issues). Our retention rate is 90 percent at 12 months because we match carefully upfront, not because we have a hard contract.
EU AI Act applies to AI systems used in EU even if developed in US. Our engineers are EU-based, with experience navigating: risk classification (minimal/limited/high/unacceptable), conformity assessment, transparency obligations, data governance. For high-risk AI (health, hiring, credit scoring), we know what documentation is required and how to design for compliance from day one. US teams shipping AI products to EU need this — we have the local expertise.
30-day notice. No minimum hours, no minimum months, no auto-renew. You can engage for 4 weeks of surge work and end with 30-day notice. You can scale up from 1 to 5 engineers and back. Most clients stay 6+ months because the model works, not because of contractual lock-in. We measure success by retention and renewal, not by clauses.
Tell me your stack, seniority and timezone. We confirm fit within 24 hours, propose 2 to 3 candidates, ready to start within 7 days.
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