Position Overview:
We are seeking a Nearshore ML Engineer based in Costa Rica to support the development, enhancement, and operationalization of Generative AI and LLM-driven solutions. This role is ideal for a mid-junior ML Engineer (1–3 years) who is eager to grow in GenAI, LLMs, and modern ML workflows.

The nearshore ML Engineer will focus on development support, experimentation, model implementation, and operational assistance, while collaborating with senior, U.S.-based ML Engineers who lead architecture, research, and complex model design.

Strong English proficiency, time zone alignment, and hands-on ML / GenAI exposure.

Core Responsibilities

• Support development and implementation of ML and LLM-based solutions, with a focus on GenAI use cases.
• Assist with prompt engineering, prompt optimization, and prompt testing for LLM-powered applications.
• Work with pre-trained and fine-tuned LLMs to support inference, evaluation, and iteration.
• Contribute to RAG pipelines, embeddings, and vector-based retrieval workflows.
• Assist in model testing, validation, and performance monitoring.
• Collaborate with U.S.-based ML Engineers on deployments, enhancements, and iterative improvements.
• Support operational tasks related to ML workflows, deployments, and ongoing optimization.
• Document models, prompts, experiments, and workflows to support knowledge sharing.

Hard Requirements / Must-Have Skills

• Experience: 1–3 years of hands-on experience in Machine Learning or applied AI.
• Programming: Strong proficiency in Python.
• GenAI & LLM Exposure (Strong Preference):

• • Hands-on experience working with Generative AI
  • Exposure to multiple LLMs (e.g., OpenAI GPT, Anthropic Claude, Meta LLaMA, Google Gemini)
  • Practical experience with prompt engineering and prompting techniques

• ML Foundations: Understanding of core ML concepts, model evaluation, and data preprocessing.
• Frameworks: Familiarity with PyTorch, TensorFlow, Scikit-learn, or Hugging Face (hands-on experience preferred).
• Data: Ability to work with structured and unstructured data.
• Language & Communication: Strong English proficiency (written and verbal).
• Work Style: Eager to learn, adaptable, and comfortable working in a fast-evolving AI environment.

Preferred Qualifications

• Exposure to RAG architectures, embeddings, and vector databases.
• Familiarity with NLP and/or multimodal ML use cases.
• Experience supporting ML deployments or production workflows.
• Experience working with distributed or globally located engineering teams.
• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field (or equivalent experience).