Machine Learning Operations Engineer (MLOps)

Role overview Build and operate the infrastructure that moves AI and machine learning systems into production, including autonomous models and multi-agent workflows. You’ll work across cloud platforms, deployment automation, observability, security, and model governance while collaborating with AI teams and U.S.-based colleagues.

Responsibilities - Design, deploy, and maintain production pipelines for AI models and agent workflows, using orchestration tools such as LangGraph, CrewAI, or n8n. - Build and support AI infrastructure on GCP or AWS, including managed ML services, serverless functions, and data storage. - Automate integration, deployment, and continuous training with Git, Docker, and Kubernetes. - Monitor model and system health, including drift, latency, and decision quality, using observability tools such as Prometheus, Grafana, CloudWatch, Langtrace, or AgentOps. - Apply access controls, encryption, prompt injection defenses, and safety guardrails; support privacy and compliance needs. - Work with AI teams on data quality, version control, and responsible model practices.

Requirements - At least four years of MLOps or DevOps experience, including one or more years deploying, evaluating, and managing production AI/ML models or agent workflows. - Hands-on experience with either GCP services such as Vertex AI, BigQuery, and Cloud Functions, or AWS services such as SageMaker, S3, and Lambda. - Proficiency in Python, Bash, Terraform, Docker, and Kubernetes, plus familiarity with ML or agent orchestration tools. - Experience monitoring LLM systems, tracking data drift, and maintaining operational reliability. - Understanding of cloud security, data privacy, and responsible AI practices such as bias detection and explainability. - Advanced written and spoken English for real-time collaboration across U.S. time zones.

Benefits and work setup - Fully remote contractor role, open to contractors based in the United States. - Compensation in U.S. dollars, paid time off and holidays, and health insurance assistance. - Opportunity to work with U.S.-based teams on production AI systems.