Machine Learning, Data & AI Talent Pool

Location: Latin America only (Remote)
Work Type: Full-time / Contract (varies by role)

We are building a curated Talent Pool of experienced professionals across Machine Learning, Data Engineering, Data Science, MLOps, and Applied AI. By joining our pipeline, you’ll be considered for multiple opportunities with our technology clients—ranging from startups to enterprise-level companies—based on your expertise, seniority, and industry focus.

About the Talent Pool

We are looking to connect with strong technical talent who have hands-on experience designing, developing, and deploying data-driven and AI-powered solutions. If you specialize in any area of the Data & AI ecosystem, we’d love to learn more about your background and match you with upcoming projects or full-time roles.

Key Areas of Expertise We’re Looking For

1. Machine Learning & Applied AI

Building and deploying ML models (supervised, unsupervised, NLP, CV, deep learning).
Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, Hugging Face.
Model evaluation, experimentation, fine-tuning, and optimization.
Experience with GenAI, LLMs, embeddings, RAG, prompt engineering, or agentic workflows is a plus.

2. Data Engineering

Building scalable data pipelines and ETL/ELT workflows.
Strong SQL and Python.
Cloud-native data stack: AWS, Azure, GCP, Snowflake, BigQuery, Databricks, Redshift, or similar.
Stream processing or distributed systems (Kafka, Spark, Flink).

3. MLOps / DataOps

Model deployment, monitoring, CI/CD for ML, model registries, feature stores.
Experience with tools like MLflow, Kubeflow, SageMaker, Vertex AI, Databricks ML.
Strong understanding of production ML lifecycle.

4. Data Science & Analytics

Statistical modeling, experimentation (A/B testing), forecasting, segmentation.
BI tools and data visualization (Tableau, Power BI, Looker).
Experience translating business problems into analytical solutions.

General Requirements

5+ years of experience in one or more ML/Data/AI tracks.
Strong Python skills.
Cloud experience (AWS, Azure, or GCP).
Solid understanding of data modeling, pipelines, and best practices in production environments.
Ability to work remotely, communicate clearly, and collaborate asynchronously.
English proficiency (written and spoken).

Nice-to-Have Skills

Experience with LLMs in production environments.
Familiarity with RAG, vector databases, or agent frameworks.
Experience working in startups, fast-paced environments, or distributed teams.
Exposure to data governance, security, compliance, or responsible AI.

How to Join the Talent Pool

If you're interested, apply through this posting and complete the short questionnaire. Your details will help us understand your profile, tech stack, and project preferences so we can match you with the most relevant opportunities.