Machine Learning Operations Engineer (MLOps)
Builds and runs production infrastructure for agentic AI: deploying multi-agent pipelines (LangGraph, CrewAI, n8n) on GCP or AWS, automating CI/CD and continuous training with Docker/Kubernetes/Terraform, monitoring model drift and performance, and enforcing security and AI governance. 100% remote contractor role based in Latin America collaborating with US teams.
- Compensation
- Not disclosed
- Location
- Latin America
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 1+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 4+ years professional experience in MLOps or DevOps
- 1+ year deploying, evaluating, and managing production AI/ML models and agentic workflows
- Deep hands-on experience with GCP (Vertex AI, Cloud Storage, BigQuery, Cloud Functions) or AWS (SageMaker, S3, Redshift, Lambda)
- Proficiency in Python, Bash, Terraform, Docker, and Kubernetes
- Experience with AI/ML orchestration tools (PyTorch, LangGraph, CrewAI, n8n)
- Hands-on expertise monitoring LLM execution and data drift using tools like Langtrace, AgentOps, Prometheus, or CloudWatch
- Understanding of cloud security protocols (IAM, data privacy) and ethical AI principles (bias detection, explainability, compliance)
- Advanced English (written and spoken) for real-time collaboration with U.S.-based teams
Full description
Engagement Type: Full-Time / Contractor
About CodeRoad
CodeRoad is a technology execution partner trusted by companies across North America since 2002. We work alongside our clients to engineer, modernize, and scale the technology that powers their businesses, combining deep technical expertise, specialized teams, and shared accountability for outcomes. Our capabilities span product engineering, platform engineering, legacy modernization, and AI engineering.
Our engineers, designers, delivery leaders, and technology specialists collaborate through Velocity Studios: specialized teams developed within CodeRoad to deliver complex technology initiatives. Being assigned to a project means you become part of a collaborative environment where you can strengthen your expertise, take ownership of meaningful work, and grow alongside experienced professionals.
Why Build Your Career at CodeRoad?
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High-Velocity Work & Client Exposure: We work close to the technology, teams and roadmaps that move real outcomes. Direct engagement with U.S. client teams and roadmaps.
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Career Continuity & Long-Term Opportunity: Your career here can outlast any single project. Continuous multi-project pipeline; long-tenure CodeRoaders.
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Engineering Autonomy & Technical Mastery: We invest in the skills that shape what's next. Agentic AI tools and training, Coursera Enterprise, Studio practice labs, L1-L7 Career Framework.
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High-Trust Employee Experience: You grow in an environment built on trust, transparency and recognition. High-touch onboarding, transparent leadership communication, community recognition, psychological safety.
Key Responsibilities
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Deploy & Orchestrate Agentic Pipelines: Design, deploy, and manage scalable, production-grade deployment pipelines for autonomous AI models and multi-agent workflows (LangGraph, CrewAI, n8n).
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Manage Multi-Cloud AI Infrastructure: Build and maintain resilient cloud environments on GCP (Vertex AI, Cloud Functions, BigQuery) or AWS (Amazon SageMaker, AWS Lambda, S3).
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Automate CI/CD & Continuous Training: Establish automated pipelines for Continuous Integration, Continuous Deployment (CI/CD), and Continuous Training (CT) using , , and .