AI Engineer

Virtasant · Dominican Republic, United States · posted today ago

engineering python java machine-learning qa remote jobs in united states

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About this role

About Virtasant

Virtasant is a global technology services company with a network of over 4,000 technology professionals across 130+ countries. We specialize in cloud architecture, infrastructure, migration, and optimization, helping enterprises scale efficiently while maintaining cost control.

Our clients range from Fortune 500 companies to fast-growing startups, relying on us to build high-performance infrastructure, optimize cloud environments, and enable continuous delivery at scale.

About the role

Our client develops a powerful AI-powered training and communication SaaS platform that gives frontline workers the knowledge they’re missing to drive measurable business performance.

You will be a fully remote, hands-on contributor on our client's Development Team, building features and products primarily across Agentic AI and backend services, with occasional contributions to frontend user interfaces when needed. This role takes clear ownership of well-defined problems and delivers high-quality solutions with confidence and independence. Working closely with peers across the Development Team, you will contribute to technical discussions, keep pace with a fast-moving codebase, and bring genuine care for the craft to everything you build.

What You’ll Do

Design, develop, test, deploy, and support high-quality code:
- Building, running, and consuming MCP servers (tool design, auth/OAuth, multi-tenant security, deployment)
- Building LLM agents with tool/function calling and multi-agent orchestration (e.g., A2A or similar)
- Testing and evaluating agent behaviour, plus observability in production
Contribute to architecture and design discussions, and help evaluate trade-offs around scaling, reliability, and maintainability.
Develop backend services in Python (for AI and Agent work) and Java (for integrating with our client's core platform), with attention to performance and data quality.
Use AI coding tools (e.g., Cursor, Claude Code, GitHub Copilot) as part of your daily workflow to improve productivity and code quality.
Participate in code reviews, giving and receiving feedback that improves the quality and consistency of the codebase.
Identify and flag technical debt, and contribute ideas for how to address it.
Break down development tasks into clear, manageable pieces and execute them reliably.
Collaborate with Product Management and Quality Assurance to refine requirements and ensure your work meets a high bar for quality.
Share what you're learning — whether that's a new pattern, a tool, or a better way of doing something — with the people around you.
Other relevant duties as required.

What We’re Looking For

Must-Have Experience

Be based in Brazil, Colombia, the Dominican Republic, or Mexico.
Strong level of English, written and spoken.
4+ years of backend-heavy software development experience with Java and Python, with extensive usage of Agentic AI.
Solid experience in Java and Python, including working with data structures, relational databases, and SQL.
Hands-on experience with AI coding assistants (such as Cursor, Claude Code, or GitHub Copilot)as part of a daily development workflow.
Familiarity with cloud platforms (especially GCP) and application infrastructure concepts — containers, caching, and distributed systems — is an asset.
Experience working with Microservices
Comfortable working independently on well-defined problemsand seeing them through to completion.
Strong attention to detail and a commitment to unit and functional testing.
Experience building AI-driven applications, agentic products and features, working with LLMs, or using frameworks like LangChain or Google ADK:
- Hands-on production experience building, running, and consuming MCP servers, including tool design, auth/OAuth, multi-tenant security, and deployment.
- Building LLM agents with tool/function calling and multi-agent orchestration (e.g., A2A or similar)
- Testing and evaluating agent behaviour, plus observability in production
Computer Science, Engineering, or similar technical aptitude gained through post-secondary education, hands-on experience, or both.