Who you are

• 4+ years of experience in MLOps, ML engineering, or similar roles contributing to the design and implementation of applications and infrastructure
• Hands-on experience deploying ML systems to production and managing end-to-end ML pipelines
• Strong expertise in AWS (or similar cloud environments), Docker, and Kubernetes
• Experience with workflow orchestration tools such as Airflow, Dagster, or Kubeflow
• Familiarity with deployment and monitoring tools such as MLflow, SageMaker, TensorFlow Serving, or TorchServe, and experience with drift detection systems
• Strong Python development skills, with a solid understanding of software engineering practices (testing, logging, version control, code review)
• Experience developing APIs or services using frameworks such as Flask or FastAPI
• Familiarity with frameworks such as LangChain and LlamaIndex, with practical experience integrating agentic workflows (planning, memory, tool use, orchestration)
• Experience with Retrieval-Augmented Generation (RAG), LLMOps practices, and prompt engineering/tuning for production use cases
• Strong communicator who works effectively across teams and mentors peers
• Proactive, growth-oriented, and motivated to advance applied AI and agent systems in production

What the job involves

• We’re looking for a Senior MLOps Engineer to help design, scale, and productionize the next generation of ML and agentic AI systems
• In this role, you’ll not only build pipelines and infrastructure but also architect and deploy agent-driven solutions that power intelligent search, content automation, and next-gen copilots
• This position blends MLOps expertise with applied AI engineering, giving you the opportunity to design robust architectures, operationalize state-of-the-art agent frameworks, and contribute to high-impact systems in production
• Design and architect world-class technical solutions leveraging cloud technologies and modern development tools
• Set and uphold high standards for technical architectures, systems design, and operational excellence
• Drive system reliability, scalability, and maintainability across ML and agent deployments
• Build pipelines for training, evaluation, deployment, and monitoring of ML models and agent workflows
• Deploy agents and models into production with Docker, Kubernetes, and CI/CD systems
• Extend CI/CD to LLMOps, including IaC automation (Terraform) and safe release strategies
• Implement observability for models and agents: health metrics, drift detection, and behavioral monitoring
• Build dashboards and alerting to ensure trust in deployed ML/agent systems
• Support reproducibility and governance for ML/agent lifecycle management
• Partner with ML Engineers, Data Scientists, and Backend Engineers to operationalize ML and agent solutions
• Build shared libraries, reusable infrastructure components, and tools that enhance developer experience
• Share technical proposals, mentor peers, and contribute to a culture of engineering excellence
• You’ll collaborate with your teams virtually across the UK, India, and North America (we’re just a Zoom call and Slack message away!) with core sync hours and focus time for headsdown work 🏾 during the workday

Benefits

• Robust Health Benefits
• Extended Parental Leave
• Employee Stock Options
• Professional Mastery Budget
• Weekly Loopio Socials
• Annual Company Retreat
• Generous Paid Time Off Program
• Team Wellness Activities