# Machine Learning Operations Engineer (MLOps) at CodeRoad

> Remote · Latin America · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/greenhouse%3Acoderoad%3A4110962009 (read 2026-11-08).

## Facts (observed)

- Company: [CodeRoad](https://alderhq.app/companies/coderoad)
- Location text: Latin America; Remote
- Where it can be done from (inferred): Remote · Latin America (evidence: "Location: Latin America; Remote")
- Posted: 2026-01-26 (286 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://job-boards.greenhouse.io/coderoad/jobs/4110962009
- Skills listed (inferred): Python, AWS, GCP, Kubernetes, Docker, Terraform, PyTorch, LLMs

## Pay and sponsorship

- Posted pay: not posted

## Description (the employer's text)

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?

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.

Career Continuity & Long-Term Opportunity: Your career here can outlast any single project. Continuous multi-project pipeline; long-tenure CodeRoaders.

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.

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

Deploy & Orchestrate Agentic Pipelines: Design, deploy, and manage scalable, production-grade deployment pipelines for autonomous AI models and multi-agent workflows (LangGraph, CrewAI, n8n).

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).

Automate CI/CD & Continuous Training: Establish automated pipelines for Continuous Integration, Continuous Deployment (CI/CD), and Continuous Training (CT) using Git, Docker, and Kubernetes.

Monitor & Optimize Performance: Implement specialized observability frameworks (Langtrace, AgentOps, Prometheus, CloudWatch, Grafana) to track model drift, latency, and autonomous decision accuracy in real time.

Enforce Security & Guardrails: Implement robust security architectures including IAM, data encryption, prompt injection defense, and automated safety guardrails for compliance (GDPR).

Drive Data & Model Governance: Collaborate with cross-functional AI teams to establish data quality standards, version control, and ethical AI safeguards across the operational lifecycle.

### What You Bring

Core Experience: 4+ years of professional experience in MLOps or DevOps, with 1+ year dedicated to deploying, evaluating, and managing production AI/ML models and agentic workflows.

Cloud Mastery: Deep hands-on experience with GCP (Vertex AI, Cloud Storage, BigQuery, Cloud Functions) or AWS (SageMaker, S3, Redshift, Lambda).

Technical & Containerization Stack: Proficiency in Python, Bash, Terraform, Docker, Kubernetes, and AI/ML orchestration tools (PyTorch, LangGraph, CrewAI, n8n).

Observability & Analytics: Hands-on expertise monitoring LLM execution, tracking data drift, and maintaining system health using tools like Langtrace, AgentOps, Prometheus, or CloudWatch.

Security & Ethical AI Mindset: Solid understanding of cloud security protocols (IAM, data privacy) and ethical AI principles (bias detection, explainability, compliance).

Language Proficiency: Advanced English skills (both written and spoken) are mandatory for seamless real-time collaboration with U.S.-based teams across East/West Coast time zones.

### What You’ll Love

USA Contractor / 100% Remote

Competitive Pay (USD)

Holidays Off & Paid Time Off

Health insurance assistance program

Opportunity to work with high-growth US-based companies and cutting-edge tech stacks.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Acoderoad%3A4110962009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.