Job Summary
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.
The core of the role is traditional machine learning and MLOps — classification, regression, clustering, anomaly detection, forecasting, computer vision, and recommender systems, delivered as production services on managed cloud platforms. LLM and GenAI work is a meaningful but secondary part of the role — using tools such as Claude (via API and Claude Code), other enterprise LLMs, and agentic coding assistants to accelerate development and to deliver targeted GenAI features (RAG, summarization, structured extraction) where they add real business value over a classical ML approach.
Key Responsibilities
The engineer will be expected to:
• 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.
Skill Requirements
Machine Learning — Traditional ML Core
Strong, hands-on experience across most of the following:
• Supervised learning: classification and regression on tabular data using gradient boosting (XGBoost, LightGBM, CatBoost), linear models, and tree ensembles.
• Unsupervised learning: clustering (k-means, DBSCAN, hierarchical), dimensionality reduction, and anomaly detection for fraud, quality, or operational use cases.
• Time-series forecasting: classical methods (ARIMA, ETS, Prophet) and modern approaches (gradient-boosted forecasting, deep learning where justified).
• Computer vision: image classification, object detection, and segmentation using PyTorch or TensorFlow, including use of pretrained backbones and Vertex AI / Azure ML vision services.
• Recommender systems: collaborative filtering, content-based, and hybrid approaches; familiarity with embedding-based retrieval.
• Solid grounding in evaluation methodology: appropriate metrics per problem type, cross-validation, calibration, bias/variance diagnosis,