Hire Dedicated AI Engineers & ML Engineers
Senior AI engineers who ship production LLM systems, RAG pipelines, and ML infrastructure — not just notebook prototypes. Python, PyTorch, LangChain, OpenAI API, Hugging Face, and MLOps at scale. Available in 48 hours.
What Our AI Engineers Build in Production
Production AI systems with sub-200ms latency, proper evaluation frameworks, and monitoring dashboards — built to run without babysitting.
LLM Application Development
Production LLM systems using OpenAI, Anthropic, and open-source models — with prompt engineering, structured output, function calling, and cost optimization baked in.
RAG Pipeline Engineering
Retrieval-Augmented Generation pipelines with document chunking, embedding strategies, vector store selection (Pinecone/Weaviate/Chroma), and hybrid search for high-accuracy retrieval.
AI Workflow Automation
Multi-agent orchestration with LangChain, LangGraph, and CrewAI — autonomous agents that handle complex, multi-step business processes without human intervention.
Custom Model Training & Fine-tuning
LoRA/QLoRA fine-tuning of foundation models for domain-specific tasks — legal, medical, finance, and custom enterprise data. Evaluation frameworks included.
MLOps & Model Infrastructure
End-to-end ML pipelines: data versioning (DVC), experiment tracking (MLflow/W&B), model serving, A/B testing, and drift monitoring with automated retraining.
AI API Integration
Clean, production-grade integrations with OpenAI, Anthropic, Cohere, and Replicate — with rate limit handling, retry logic, caching, and token cost management.
Full AI/ML Stack Coverage
Engagement Models for AI Teams
Embed a senior AI engineer, build an AI team, or deliver a defined AI system.
Full-Time
A dedicated senior AI engineer embedded in your product team — driving your AI roadmap, building production systems, and attending your sprint ceremonies.
- Dedicated to your team only
- Daily progress sync
- Full LLM/ML system ownership
- US timezone overlap
Part-Time
A senior AI engineer for focused sprints — ideal for adding AI features to an existing product or building a RAG pipeline alongside your engineering team.
- Focused sprint blocks
- Weekly architecture review
- Async-first workflow
- Upgradeable to full-time
Project-Based
End-to-end delivery of a defined AI system — chatbot, RAG pipeline, recommendation engine, or ML model — with handover documentation and monitoring setup.
- Fixed deliverables
- Milestone-based billing
- Model card & documentation
- 30-day post-launch support
AI Engineer on Your Team in 48 Hours
From brief to first commit — a streamlined process designed for technical teams who don't have time for lengthy recruiting cycles.
Share Your AI Brief
Describe your AI use case, data environment, model preferences, and production requirements. We'll ask the right technical questions.
Profiles in 24h
We send matched AI engineer profiles within 24 hours — specialists in your specific stack (LLM, computer vision, NLP, MLOps).
Technical Evaluation
All AI candidates pass our system design review covering RAG architecture, model evaluation, and production inference optimization.
Proof of Concept
Start with a defined PoC sprint. Validate the engineer's approach and code quality before committing to production work.
Production Delivery
Engineer ships production-ready AI system — with monitoring, logging, and cost controls. Live within 48 hours of onboarding.
Why Infonza AI Engineers Deliver at Production Scale
Production AI, Not Notebooks
Our AI engineers have shipped production LLM systems — not just Jupyter notebooks. They understand inference optimization, API cost management, and monitoring at scale.
Data Security & Privacy
All AI engineers operate under NDA with explicit data processing agreements. We work with clients' PII-sensitive data using privacy-first architecture patterns.
US Timezone Overlap
AI engineers aligned to EST morning overlap. Real-time collaboration for architecture reviews and sprint planning — not just async code drops.
LLM Cost Optimization
Our engineers build AI systems that are cost-effective at scale — using prompt caching, model routing, and retrieval strategies that cut inference costs by 40–60%.
Evaluation-First Culture
Every AI system we build includes a proper evaluation framework — automated evals, human review workflows, and regression testing before any model or prompt change reaches production.
Ready to build your AI system in production?
Share your AI use case and we'll match you with a vetted senior AI engineer in 24 hours.
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Find Your Perfect AI Engineer Today
Production LLM systems, RAG pipelines, and MLOps — built by engineers who've shipped AI at scale. Get matched profiles in 24 hours.