NEARSHORE AI ENGINEERS
Production-ready AI engineers from Costa Rica with full US timezone overlap. LLM integration, RAG pipeline, and GenAI application expertise at 40-60% below domestic rates, with a 90-day replacement guarantee on every placement.
Senior AI Engineers.
Real-Time Collaboration.
Costa Rica produces AI engineers with 4-8 years of experience building production LLM applications, RAG pipelines, and GenAI systems. They operate UTC-6 year-round with full overlap with US business hours and cost 40-60% less than a domestic equivalent.
A nearshore AI engineer is a machine learning and generative AI specialist based in a geographically close country (Costa Rica for US companies) rather than a distant timezone. They design and build LLM-powered applications, RAG pipelines, fine-tuned models, and agentic AI workflows using Python, PyTorch, LangChain, and cloud AI platforms. They join your standups live, respond to production incidents in real time, and communicate in English without coordination lag. Kore BPO sources, vets, and places these engineers so you skip months of domestic recruiting and avoid the overhead of managing a foreign entity yourself.
We Don’t Send Profiles. We Send the Right Engineer.
Our technical screeners have built production LLM applications on OpenAI, Anthropic, and open-source models. Every candidate we submit has passed an async assessment covering RAG architecture design, prompt engineering, model fine-tuning approaches, and production reliability under real-world constraints.
- ✓ Technical screen: Python, PyTorch, LangChain, RAG, fine-tuning, and vector database depth
- ✓ Platform depth: AWS Bedrock, GCP Vertex AI, or Azure AI Foundry matched to your environment
- ✓ English communication check: written and spoken fluency at production incident level
- ✓ Evaluation and safety: LLM evaluation frameworks, guardrails, and output quality monitoring
What Our AI Engineers Build With
Every candidate is assessed on the specific tools and frameworks your team runs. We screen for production depth, not just tool names on a resume.
Core Languages
Python (PyTorch, TensorFlow, JAX, NumPy, pandas), Rust for inference optimization, TypeScript for full-stack AI application layers
LLM Frameworks
LangChain, LlamaIndex, LangGraph for agentic workflows, Semantic Kernel, Haystack; OpenAI API, Anthropic API, Cohere, and Mistral integrations
RAG and Retrieval
Vector databases: Pinecone, Weaviate, Chroma, Qdrant, pgvector; embedding models, hybrid search, reranking with Cohere Rerank and ColBERT
Fine-Tuning and Training
LoRA, QLoRA, PEFT, DPO, RLHF pipelines; Hugging Face Transformers and PEFT library; SFT on custom domain datasets at production scale
Cloud AI Platforms
AWS Bedrock, SageMaker JumpStart; GCP Vertex AI, Model Garden; Azure AI Foundry, Azure OpenAI Service; deployment on EKS, GKE, and AKS
MLOps and Observability
MLflow, Weights & Biases, DVC for experiment tracking; LangSmith, Langfuse, and Arize for LLM observability and evaluation pipelines
Serving and Inference
vLLM, TGI, BentoML, Ray Serve, Triton Inference Server; ONNX Runtime and TensorRT for latency optimization; batch and real-time serving patterns
Agentic and Multimodal
Multi-agent orchestration with LangGraph and CrewAI; function calling and tool use; multimodal pipelines with vision models and audio transcription
From Requirements to First Sprint in 14 Days
Our process is designed to put a production-ready AI engineer on your team faster than a domestic search would surface a first phone screen.
Discovery Call
We map your AI stack, model types, use cases, and team structure in 45 minutes.
Candidate Sourcing
We pull from our vetted Costa Rica bench and run framework-specific technical screens.
Profile Delivery
You receive 2-3 shortlisted profiles with video intros and assessment results in 72 hours.
Your Interview
Run your own technical interview. Most clients use one 90-minute session.
Offer and Start
We handle contracts and onboarding logistics. Engineer starts contributing in sprint one.
Nearshore Fits These AI Teams Well
Nearshore AI engineering is the right move for most US product teams. Here is when it fits and when it does not.
STRONG FIT – Nearshore Works Well When
- ✓You need a dedicated AI engineer who builds deep context on your product and LLM stack
- ✓Your work requires real-time collaboration with a US engineering or product team
- ✓You are building production RAG systems, LLM integrations, or fine-tuned model pipelines
- ✓You want to reduce AI engineering costs without sacrificing technical depth or communication quality
- ✓You need a long-term team member who can own model reliability, evaluation, and iteration
LESS IDEAL – Consider Alternatives When
- ✕You need a one-time proof-of-concept built in under three weeks with no ongoing ownership
- ✕Your compliance framework prohibits all AI model processing outside US infrastructure
- ✕You need someone on-site in a US office for the majority of their working hours
- ✕You want a short-term contractor, not a long-term embedded team member
- ✕Your team does not yet have an AI product roadmap or defined use case in place
Nearshore vs. US Hiring Costs
All-in costs through Kore BPO include placement, payroll management, benefits administration, and account management support. No upfront search fees.
| Experience Level | US Market (Annual) | Nearshore via Kore BPO | Typical Savings |
|---|---|---|---|
| Mid-Level (3-5 yrs) | $140,000 – $175,000 | $70,000 – $90,000 | $70,000 – $85,000/yr |
| Senior (5-8 yrs) | $175,000 – $230,000 | $90,000 – $120,000 | $85,000 – $110,000/yr |
| Staff / Principal (8+ yrs) | $230,000 – $290,000 | $120,000 – $150,000 | $110,000 – $140,000/yr |
Ranges reflect all-in annual cost including salary, benefits, payroll management, and Kore BPO account management. US figures are 2026 national averages across major tech markets. Nearshore ranges reflect Costa Rica placements specifically.
Need a Precise Cost Estimate?
Tell us your required AI stack, seniority level, and team size. We will provide a detailed cost breakdown within one business day.
What Our Clients Say
“We needed someone who could build LangChain pipelines and own our RAG evaluation loop from day one. The engineer Kore BPO placed had done this before in a production environment. He was shipping improvements within the first two weeks.”
“The time zone overlap is the thing that makes nearshore work for AI engineering. Our engineer is in stand-up every morning, responds to eval pipeline issues the same business day, and has become a core voice in our model improvement process.”
Common Questions About Nearshore AI Engineers
What is a nearshore AI engineer?
A nearshore AI engineer is a machine learning and generative AI specialist based in a geographically close country such as Costa Rica rather than a distant offshore location. For US companies, nearshore means 0-2 hours of time zone difference, full real-time collaboration during business hours, and Latin American communication and work culture. Nearshore AI engineers build LLM-powered applications, RAG pipelines, fine-tuned models, and agentic workflows using Python, PyTorch, LangChain, and cloud AI platforms. Kore BPO places these engineers from Costa Rica at 40-60% below US market rates with a 90-day replacement guarantee.
How much does a nearshore AI engineer cost?
Through Kore BPO, all-in annual costs for a nearshore AI engineer from Costa Rica typically range from $70,000 for a mid-level engineer (3-5 years of production LLM and ML experience) to $120,000 for a senior engineer (5-8 years) with deep RAG architecture, fine-tuning, and cloud AI platform expertise. Staff and principal-level engineers with 8 or more years can reach $120,000 to $150,000 all-in. These figures include the engineer’s salary, benefits, payroll management in Costa Rica, and Kore BPO account management support. There are no upfront search fees.
How long does placement take?
With Kore BPO, the typical timeline is 10 to 14 business days from discovery call to first candidate presentation. You receive 2-3 fully vetted profiles with video introductions and technical assessment results. Your interview and offer process adds 3-5 business days in most cases, putting the AI engineer contributing to your production systems within three to four weeks of starting the search.
What AI frameworks and platforms do Costa Rica engineers use?
Costa Rica AI engineers commonly work with Python, PyTorch, and LangChain as their primary stack. For LLM integrations they work with OpenAI API, Anthropic API, and Hugging Face models. RAG pipelines are typically built with LlamaIndex or LangChain combined with vector databases such as Pinecone, Weaviate, or pgvector. Cloud AI experience spans AWS Bedrock, GCP Vertex AI, and Azure AI Foundry. Kore BPO screens specifically for the frameworks and platforms your team runs rather than placing generalists.
Does Kore BPO offer a placement guarantee?
Yes. Kore BPO backs every placement with a 90-day replacement guarantee. If the AI engineer does not meet your expectations for technical skills or performance within the first 90 days, we re-run the full search and placement at no additional cost. The guarantee covers technical mismatches and soft-skill or cultural fit issues confirmed in writing between your team and your Kore BPO account manager.
Learn More About Nearshore AI Engineering
HIRE YOUR NEARSHORE AI ENGINEER
Get pre-screened candidates from Costa Rica on your desk within 72 hours. 90-day replacement guarantee on every placement.
GET STARTED TODAYNo upfront fees | 90-day replacement guarantee | Dedicated account manager