Senior AI/MLOps Engineer

About the Role We are looking for a Senior AI/MLOps Engineer to design, build, deploy, and operate end-to-end machine learning solutions on Google Cloud Platform (Vertex AI) and Microsoft Azure (Azure ML). This is a full-stack ML role: the engineer owns the lifecycle from problem framing and data preparation through model development, deployment, monitoring, and retraining, with GenAI/LLM work as a meaningful but secondary component. What You'll Do

• Translate business problems into well-scoped ML use cases, choosing the right technique (supervised, unsupervised, forecasting, CV, recommender, or LLM) for the problem — not the other way round.
• Own the full ML lifecycle: data ingestion and preparation, feature engineering, model training and evaluation, deployment, monitoring, and retraining.
• Design and implement production pipelines on Vertex AI and Azure ML using managed PaaS/SaaS services as the default, avoiding unnecessary custom infrastructure.
• Build and maintain CI/CD workflows for ML (code, data, and model artifacts), including automated training, testing, validation, and deployment.
• Implement model monitoring for performance, data drift, concept drift, and operational health; define and act on retraining triggers.
• Integrate ML services with enterprise applications, APIs, event streams, and data platforms (BigQuery, Cloud Storage, Azure Data Lake, Synapse, etc.).
• Apply GenAI/LLM capabilities (Claude, Vertex AI model garden, Azure OpenAI) where appropriate — for example, document understanding, RAG over enterprise knowledge, structured extraction, or internal developer productivity via Claude Code and similar tools.
• Partner with data engineers, software engineers, product managers, and business stakeholders to deliver solutions that are reliable, explainable, and maintainable. What We're Looking For
• Machine Learning — Traditional ML Core: strong hands-on across supervised learning (classification and regression) with gradient boosting (XGBoost, LightGBM, CatBoost), linear models, and tree ensembles.
• Unsupervised learning: clustering (k-means, DBSCAN, hierarchical), dimensionality reduction, and anomaly detection.
• Time-series forecasting: classical methods (ARIMA, ETS, Prophet) and modern approaches.
• Computer vision
• Recommender systems
• Experience building end-to-end ML solutions including data ingestion, feature engineering, model training, deployment, monitoring, and retraining.
• Proficiency designing production pipelines on Vertex AI and Azure ML using managed services; ML CI/CD and automated training/testing/validation/deployment.
• Model monitoring for performance, data drift, concept drift, and retraining triggers.
• Familiarity with GenAI/LLM capabilities (Claude, Vertex AI model garden, Azure OpenAI) for document understanding, RAG, structured extraction, or developer productivity.
• Strong collaboration with data engineers, software engineers, product managers, and business stakeholders; ability to deliver reliable, explainable, and maintainable solutions.