Machine Learning and Artificial Intelligence Scientist

We are seeking a Senior Machine Learning and Artificial Intelligence Scientist to lead the development and production deployment of advanced ML and AI solutions that deliver measurable business impact. This role requires a proven track record of taking models from problem definition and experimentation through production deployment, adoption, monitoring, and continuous improvement. The successful candidate will design and implement machine learning, generative AI, and multi-agent solutions using complex, heterogeneous, and imperfect data structures. They will partner closely with business leaders, product owners, data engineers, software engineers, cloud architects, and technical stakeholders to translate business needs into scalable AI products and communicate technical outcomes in clear business terms. The role requires strong experience with cloud-native data and AI architectures, especially Azure and Databricks, as well as the ability to operate across AWS and Google Cloud Platform. The scientist will work with governed lakehouse, data mesh, model-serving, MLOps, LLMOps, and enterprise integration patterns to deliver secure, reliable, and maintainable AI capabilities. Technical Stack and Engineering Environment The role may work across the following technologies and patterns: Programming and data science: Python, SQL, PySpark, pandas, NumPy, SciPy, scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch, and Jupyter-based development. Data platforms: Azure Databricks, Databricks Lakehouse, Apache Spark, Delta Lake, Delta Sharing, Unity Catalog, Databricks SQL, Lakeflow Declarative Pipelines, Databricks Workflows, Lakebase, MLflow, Mosaic AI, Model Serving, Vector Search, AI Gateway, and Databricks Genie. Azure: Azure Data Lake Storage Gen2, Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Azure Event Hubs, Azure Data Factory or equivalent orchestration, Azure Functions, Azure Kubernetes Service, Azure Container Apps, Azure Key Vault, Azure Monitor, Application Insights, Microsoft Defender for Cloud, Azure API Management, Entra ID, and private networking patterns. Google Cloud: Vertex AI, Gemini, Vertex AI Model Garden, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, Google Kubernetes Engine, Cloud SQL, Secret Manager, Cloud IAM, Cloud Logging, and Cloud Monitoring. AWS: Amazon SageMaker, Amazon Bedrock, S3, Glue, Athena, Redshift, EMR, Lambda, EKS, Step Functions, CloudWatch, IAM, and related data and AI services. Generative AI and multi-agent systems: large language models, foundation models, embeddings, vector databases, retrieval-augmented generation, prompt engineering, structured outputs, function calling, tool use, agent orchestration, workflow engines, evaluation frameworks, guardrails, model routing, and human-in-the-loop controls. Data integration and governance: Fivetran, change data capture, Event Hubs, Auto Loader, APIs, batch and streaming ingestion, data contracts, schema enforcement, data quality checks, data lineage, data catalogs, access controls, row- and column-level security, and governed data products. Engineering and delivery: GitHub, GitHub Actions, Azure DevOps or equivalent CI/CD, Terraform, Docker, Kubernetes, Helm, REST APIs, FastAPI, OpenAPI, microservices, infrastructure as code, automated testing, feature flags, and release management. Observability and operations: OpenTelemetry, Azure Monitor, Application Insights, CloudWatch, Google Cloud Monitoring, Datadog or equivalent monitoring platforms, centralized logging, model performance monitoring, data drift detection, concept drift detection, latency monitoring, cost monitoring, and incident response. Analytics and business consumption: Power BI, Databricks SQL, semantic models, dashboards, governed data products, operational APIs, and embedded AI experiences. What You’ll Do Identify high-value business problems where machine learning, generative AI, or multi-agent systems can improve revenue, cost, risk,