Machine Learning Operations Engineer (MLOps)

About the role

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 Git, Docker, and Kubernetes.

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

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

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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.