Hire AI Engineers

Hire AI Engineers

Hire senior AI engineers from Latin America in 8-12 days. Build production AI systems with LLMs, RAG pipelines, vector databases, and AI-native applications that deliver real business value.

Get started →

TL;DR

Hire AI engineers in 8-12 days: Get vetted, senior-level AI engineers from Latin America who build production AI systems with LLMs, RAG pipelines, and AI-native applications. Work in US time zones, cost 40-60% less than domestic hires. Meet qualified candidates in 48 hours.

Key Benefits:

  • Fast Placement: Meet candidates in 48 hours, fully onboarded in 8-12 days
  • Senior Expertise: 5+ years software engineering + 2+ years AI/LLM experience
  • Production-Focused: Not researchers—engineers who ship AI products that scale
  • Cost Effective: $6,000-10,000/month vs $15,000-20,000+ for US AI engineers
  • Time Zone Aligned: 0-3 hour difference for real-time collaboration
  • Retention Guarantee: Free replacement within 90 days
  • Full Support: Payroll, benefits, HR, equipment, team retention

Why Hire AI Engineers Through Ideaware?

The AI engineering market is flooded with “prompt engineers” who’ve taken a ChatGPT course. You need engineers who understand production AI systems: vector databases, embedding models, RAG architectures, LLM orchestration, cost optimization, and how to build AI products that don’t hallucinate your business into the ground.

At Ideaware, we’ve vetted thousands of developers across Latin America. We maintain a pipeline of 300+ AI engineers ready to interview this week.

We Actually Vet for AI Engineering Expertise: Not just ChatGPT API calls. We test for RAG pipeline design, vector database optimization, prompt engineering, LLM fine-tuning, production deployment, cost management, and evaluation frameworks.

We Move Fast: 48 hours to candidates. 8-12 days to onboarded engineers.

We Handle Everything: Payroll, benefits, equipment, HR, retention, career development.

We Guarantee Results: Free replacement if it doesn’t work out. 2x industry average retention rate.

What Our AI Engineers Do

  • Build production AI applications using OpenAI, Anthropic Claude, open-source LLMs with proper error handling, fallbacks, and monitoring
  • Design RAG (Retrieval Augmented Generation) pipelines with vector databases, embedding models, chunking strategies, and retrieval optimization
  • Implement LLM orchestration using LangChain, LlamaIndex, or custom frameworks for complex multi-step AI workflows
  • Develop AI-native products that integrate LLMs as core features, not bolted-on chatbots
  • Optimize prompt engineering with systematic testing, versioning, evaluation metrics, and cost management
  • Deploy vector databases using Pinecone, Weaviate, Qdrant, or pgvector for semantic search and RAG systems
  • Integrate AI APIs from OpenAI, Anthropic, Cohere, Hugging Face with proper rate limiting and cost controls
  • Fine-tune and customize models for domain-specific tasks, brand voice, or specialized use cases
  • Build evaluation frameworks to measure AI system performance, accuracy, hallucination rates, and user satisfaction
  • Manage AI infrastructure on AWS Bedrock, Azure OpenAI, GCP Vertex AI, or self-hosted solutions
  • Implement safety guardrails for content filtering, PII detection, bias mitigation, and responsible AI practices

When to Hire AI Engineers

AI-Native Products: Building applications where AI is the core product, not a feature—conversational interfaces, document analysis, content generation

Enterprise RAG Systems: Internal knowledge bases, document Q&A, semantic search across company data

Workflow Automation: Intelligent process automation, document extraction, email triage, customer support automation

Content Generation: Marketing copy, personalized content, dynamic report generation, SEO optimization

Data Analysis: Natural language queries over databases, automated insights, anomaly detection

Customer Experience: AI-powered chatbots, personalized recommendations, sentiment analysis, voice assistants

Common Tech Stack & Skills

LLM Platforms:

  • OpenAI (GPT-4, GPT-4 Turbo, GPT-3.5)
  • Anthropic Claude (Sonnet, Opus)
  • Open-source (Llama 3, Mistral, Mixtral)
  • Fine-tuning (LoRA, QLoRA, PEFT)

Orchestration & Frameworks:

  • LangChain
  • LlamaIndex
  • Haystack
  • Semantic Kernel
  • Custom frameworks

Vector Databases:

  • Pinecone
  • Weaviate
  • Qdrant
  • Chroma
  • pgvector (PostgreSQL)
  • Milvus

Backend & APIs:

  • Python (FastAPI, Django, Flask)
  • Node.js (Express, NestJS)
  • REST & GraphQL APIs
  • WebSockets (streaming responses)

Cloud & Infrastructure:

  • AWS (Bedrock, SageMaker, Lambda)
  • Azure (OpenAI Service, Cognitive Services)
  • GCP (Vertex AI, PaLM API)
  • Docker, Kubernetes

Data Processing:

  • Document parsing (PyPDF, Unstructured)
  • Text chunking strategies
  • Embedding models (OpenAI, Sentence Transformers)
  • Data pipelines (Airflow, Prefect)

Evaluation & Monitoring:

  • LLM evaluation frameworks
  • Prompt testing & versioning
  • Cost tracking & optimization
  • Observability (LangSmith, Weights & Biases)

Tools:

  • Jupyter Notebooks
  • Git, GitHub/GitLab
  • VS Code, PyCharm
  • Postman, API testing

Pricing & Engagement

Senior AI Engineers: $6,000-10,000/month

  • Full-time (40 hrs/week)
  • Benefits & payroll
  • HR support
  • Equipment
  • Retention programs

Compare to US AI engineers: $180,000-240,000/year = $15,000-20,000/month

Your savings: 40-60% without sacrificing quality.

Frequently Asked Questions

How much does it cost to hire AI engineers?

Senior AI engineers cost $6,000-10,000/month including full-time employment, benefits, HR support, and retention programs. This is 40-60% less than US-based AI engineers with no recruiting fees. Note: AI engineers command premium rates (20-40% above standard software engineers) due to specialized expertise and high market demand.

How quickly can I hire AI engineers?

48 hours to vetted profiles. 8-12 days to fully onboarded. 10-15 weeks faster than traditional hiring. Given the competitive AI talent market, having a pre-vetted pipeline gives you significant speed advantages.

What skills should AI engineers have?

Essential: 5+ years software engineering + 2+ years AI/LLM experience, Python/Node.js, OpenAI/Anthropic APIs, RAG architecture, vector databases, prompt engineering, LangChain/LlamaIndex, production deployment, cost optimization, evaluation frameworks. Bonus: fine-tuning, open-source LLMs, domain expertise.

Are nearshore AI engineers as good as US developers?

Yes. Many have worked with US AI startups, 80%+ hold CS or related degrees, work in US time zones with strong English. The AI engineering field is global—talent learns from the same papers, tools, and communities regardless of location. 80% hire rate, 2x retention rate.

What is your vetting process for AI engineers?

Only 3% pass: (1) AI/LLM technical assessment (RAG design, prompt engineering, vector databases), (2) 90-min live coding building an AI feature, (3) production AI architecture review, (4) portfolio review of shipped AI products, (5) cultural fit evaluation, (6) reference checks. We filter out “prompt jockeys” and identify engineers who build production systems.

What time zones do AI engineers work in?

0-3 hour difference from US time zones. Real-time collaboration during business hours for sprint planning, AI system debugging, and prompt iteration.

Can I hire a full AI engineering team?

Yes. Solo AI engineers, pairs, or complete AI teams (2-3 AI engineers + full stack developers + ML engineer + data engineer). Many clients start with 1 AI engineer and scale to full product teams.

What if it doesn't work out?

Free replacement within 90 days. No fees or penalties. Given the specialized nature of AI engineering, cultural and technical fit is critical—we stand behind our placements.

AI Engineers vs ML Engineers: What's the difference?

AI Engineers focus on integrating and productionizing AI—building applications with LLMs, RAG systems, prompt engineering, API integration, and AI-native products. They’re software engineers who specialize in AI.

ML Engineers focus on training and deploying ML models—data pipelines, model training, feature engineering, MLOps, custom model architectures. They work closer to data science.

When to hire AI Engineers:

  • Building with existing LLMs (OpenAI, Claude)
  • RAG systems, chatbots, AI features
  • Rapid product development with AI APIs
  • Startups and fast-moving teams

When to hire ML Engineers:

  • Custom model development
  • Training models on proprietary data
  • MLOps and model lifecycle management
  • When off-the-shelf LLMs don’t meet needs

Our take: For most startups and companies building AI products in 2025, AI Engineers provide faster time-to-market using powerful pre-trained models. ML Engineers become essential when you need custom models or specialized ML infrastructure.

What ongoing support do you provide?

Full HR support (payroll, benefits), career development (AI/ML training, conferences), retention programs, operational support, strategic partnership (quarterly reviews, talent pipeline). We help AI engineers stay current in this rapidly evolving field.

What are the contract terms?

Month-to-month with 30-day notice. No long-term commitments, no placement fees. Free 90-day replacement guarantee. Flexibility matters in the fast-moving AI space.

Who owns the AI models and code?

You own 100%. All IP, code, prompts, fine-tuned models, and training data assigned to your company with NDAs and secure protocols. This includes any custom prompts, RAG architectures, or model adaptations developed for your product.

How good is the English communication?

Business-level required. Clients rate 4.7/5. Full participation in technical discussions without friction. Critical for AI engineering where explaining complex LLM behaviors and prompt iterations requires clear communication.

Do you provide junior or only senior AI engineers?

Primarily senior (5+ years software + 2+ years AI): $6,000-10,000/month. Also: mid-level (3-5 years software + 1+ year AI): $5,000-7,000/month. We rarely recommend junior AI engineers given the complexity of production AI systems.

How do you ensure AI engineers have production experience?

We specifically vet for production AI experience: engineers who’ve shipped AI products to real users, handled scaling challenges, managed LLM costs, dealt with hallucinations, implemented evaluation metrics, and debugged production AI systems. Portfolio reviews and reference checks focus on production deployments, not just side projects or research papers. We filter out pure researchers and academics in favor of engineers who ship.

Next Steps

  1. Book Discovery Call - 30 minutes to discuss your AI product needs
  2. Meet Candidates - 48 hours to receive vetted AI engineer profiles
  3. Interview & Select - Week 1, you control the process
  4. Onboard & Ship - Week 2, engineer integrated into your team
  5. Scale as Needed - Add more AI engineers or build complete AI product teams

Schedule your discovery call and meet qualified AI engineers in 48 hours.