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Aspenview Technology Partners

Machine Learning / AI Software Engineer

Latin America

Location
Latin America
Work
Not stated
Employment
Not stated
Level
Not stated
Function
Data & AI
Pay
Not stated
Posted
2 months ago

About the role

Build the Future with AspenView Technology Partners

At AspenView, we are passionate about transforming the way organizations approach technology. We specialize in creating high-performing, nearshore IT teams to help North American clients innovate faster and more efficiently.

As we continue to grow, we’re looking for exceptional people to join our team and help drive impactful change across industries.

Why Join AspenView?

At AspenView, we’re more than a nearshore IT partner—we’re a people-first, purpose-driven company that believes great culture drives great outcomes. We’re passionate about connecting talent and technology to deliver measurable value for clients—and meaningful career paths for our people.

Here’s what you can expect:

  • Competitive base
  • Flexible work model: hybrid, remote, or in-office
  • Real growth opportunities and leadership visibility
  • Inclusive, respectful culture that blends U.S. innovation with Colombian heart
  • A company that listens, invests in you, and celebrates wins together

Step into a high-impact role as a Machine Learning / AI Software Engineer, where you will drive AI-enabled decisioning for an enterprise payments and commerce platform in the travel and hospitality sector. Working in an embedded, client-side collaboration model, you will partner closely with stakeholders to translate complex business requirements into deployable, domain-tuned models that outperform generic baselines.

What you will do

ML Solution Delivery & Agentic AI

  • Develop and deploy machine learning solutions to production, translating business requirements into deployable ML.
  • Build on AWS Bedrock / AgentCore for specialized agentic use cases.
  • Support agent QA and evaluation across workflows.

Data Engineering & Pipelines

  • Build and maintain robust data pipelines that feed models.
  • Ensure strict data quality and reproducibility across the ecosystem.

Observability & Monitoring

  • Instrument comprehensive observability for AI workloads.
  • Track model performance and drift over time to ensure continuous improvement in production.

Tools & Technologies

  • Platforms & Infrastructure: AWS Bedrock / AgentCore, AI Observability tooling
  • Security & DevSecOps: MLOps, Model Governance, Agent QA/Evaluation
  • Cloud & IaC: Python, Cloud AI on AWS, Data Pipelines

What you bring

  • Experience: 5+ years in machine-learning / AI engineering, with required domain experience in the travel & hospitality industry (specifically decisioning models like fraud, dispute, and guest-servicing). You also hold a Bachelor's degree in Computer Science, Software Engineering, or equivalent practical experience.
  • Consulting / Leadership: Strong written and spoken English for seamless, embedded client-side collaboration. You are LatAm-based and aligned to U.S. Eastern Standard Time (EST).
  • Mindset: A results-driven approach focused on domain-tuned decisioning, ensuring pipelines are reliable and AI workloads are observable and well-governed.

Visa Sponsorship

AspenView does not sponsor employment visas for this role. Applicants must be permanently authorized to work in their country of residence and must not require visa sponsorship now or in the future.

Equal Opportunity Employer

AspenView is proud to be an equal opportunity employer. We believe in creating an environment where all employees feel welcome, valued, and empowered to succeed. We celebrate diversity and strive to build a culture of inclusion where all individuals, regardless of their race, color, gender, gender identity or expression, sexual orientation, disability, age, or any other characteristic, can thrive. We encourage applicants from all walks of life to join our team and make a lasting impact.

Source

Posted by Aspenview Technology Partners on their own hiring page, job-boards.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 01:21 UTC; the original is the reference.

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