Mlops Consultant / Architect

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