We’re seeking a hands-on ML/AIOps Consultant to help define and implement our machine learning and AI operations strategy. The consultant will play a key role in designing the architecture, frameworks, and processes that enable scalable, production-grade ML and AI systems — spanning classical ML, GenAI, and agentic AI scenarios.Core Responsibilities Develop and operationalize the ML/AIOps strategy, architecture, and framework Define end-to-end strategy covering classical ML, Generative AI, and Agentic AI Assess existing ML/AI processes, perform gap analyses, and recommend improvements Design and implement monitoring, automation, and CI/CD pipelines for ML models Contribute to model governance, observability, and lifecycle management Produce clear documentation, reusable assets, and best practices Collaborate with the Data Scientist in Riga and other business teams (some travel expected) Must-Have Skills Proven hands-on MLOps experience in production environments Strong knowledge of ML pipeline orchestration, e.g., Kubeflow, MLflow, Vertex AI, SageMaker, or similar Deep understanding of model deployment, monitoring, retraining, and drift detection Experience with containerized and cloud-native infrastructure (Docker, Kubernetes) Proficiency in Python, Git, CI/CD, and infrastructure-as-code tools Familiarity with GenAI and LLMOps frameworks (LangChain, Weaviate, vector DBs, etc.) Ability to bridge data science and DevOps disciplines Nice to Have Experience implementing AIOps for IT operations — anomaly detection, predictive monitoring, or automated remediation Exposure to agentic AI architectures (multi-agent frameworks, orchestration layers, etc.) Strong architectural background — can design scalable and secure ML/AIOps environments Previous consulting or advisory experience in AI/ML platform strategy