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.
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.
Identify high-value business problems where machine learning, generative AI, or multi-agent systems can improve revenue, cost, risk, productivity, customer experience, or operational performance.
Translate ambiguous business objectives into well-defined analytical problems, measurable success criteria, model evaluation plans, deployment strategies, and adoption metrics.
Design, develop, validate, and deploy production-grade machine learning models across forecasting, classification, regression, optimization, anomaly detection, recommendation, natural language processing, computer vision, time-series analysis, and other relevant use cases.
Build and deploy generative AI and multi-agent solutions that coordinate specialized agents, tools, APIs, retrieval systems, workflows, and business rules to solve complex problems.
Design agentic systems with clear task decomposition, tool permissions, state management, memory boundaries, error handling, evaluation, observability, and human escalation paths.
Develop solutions that operate reliably across structured, semi-structured, and unstructured data, including fragmented data sources, inconsistent schemas, missing values, changing definitions, and data quality issues.
Engineer robust data and feature pipelines in partnership with data engineering teams using batch, streaming, CDC, and event-driven patterns while ensuring reproducibility, lineage, validation, versioning, and reliable access to model inputs.
Build lakehouse and data mesh solutions using Delta Lake, medallion architecture, domain-oriented data products, Unity Catalog, governed workspaces, and environment separation across development, test, and production.
Architect scalable cloud-based AI solutions using Microsoft Azure, Databricks, Amazon Web Services, and Google Cloud Platform.
Design for cloud portability and resilience when appropriate, including provider abstraction, model routing, active/passive or active/active deployment, disaster recovery, data residency, and controlled cross-cloud data movement.
Apply strong software engineering practices, including modular design, unit and integration testing, code review, version control, CI/CD, containerization, infrastructure automation, API design, secure secrets management, and production release discipline.
Implement MLOps and LLMOps practices for dataset, feature, model, prompt, agent, and evaluation versioning; automated testing; deployment; monitoring; drift detection; performance evaluation; cost management; and rollback.
Establish AI evaluation frameworks that measure factuality, relevance, groundedness, safety, bias, robustness, latency, cost, tool-call accuracy, task completion, and business usefulness.
Implement appropriate safeguards for AI systems, including security, privacy, access control, responsible AI, explainability, auditability, data classification, model governance, and compliance requirements.
Evaluate models and AI systems using both technical metrics and business outcomes, such as accuracy, calibration, latency, reliability, adoption, process efficiency, revenue impact, cost reduction, and risk reduction.
Conduct controlled experiments, pilot deployments, A/B tests, champion-challenger evaluations, and post-launch assessments to validate whether solutions produce sustained business value.
Diagnose model, data, pipeline, architecture, and production issues and lead remediation through root-cause analysis and cross-functional collaboration.
Present technical findings, model behavior, limitations, risks, architecture decisions, and recommendations to business and executive stakeholders in clear, decision-oriented language.
Explain business priorities and operational requirements to technical teams and translate them into effective data, modeling, architecture, and delivery decisions.
Mentor other data scientists and engineers by promoting sound modeling practices, production discipline, technical quality, documentation, and continuous learning.
Contribute to the strategic roadmap for machine learning, generative AI, and multi-agent capabilities, including technology selection, platform standards, reusable components, reference architectures, and operating models.
Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related technical field; advanced degree preferred.
5+ years of experience developing and deploying machine learning or artificial intelligence solutions in production environments.
Demonstrated success delivering ML or AI solutions that generated measurable business impact, such as improved forecast accuracy, reduced cost, increased revenue, improved risk management, higher productivity, or better customer outcomes.
Strong experience with the complete machine learning lifecycle, including problem formulation, data preparation, feature engineering, model development, validation, deployment, monitoring, retraining, and decommissioning.
Experience developing production systems with Python, SQL, PySpark, and common machine learning frameworks and libraries.
Strong understanding of statistical modeling, machine learning algorithms, experimental design, model evaluation, uncertainty, explainability, and performance trade-offs.
Proven ability to build solutions using complex and imperfect data, including disparate sources, evolving schemas, inconsistent definitions, missing values, noisy signals, and high-volume datasets.
Experience designing and deploying cloud-based solutions using one or more of Microsoft Azure, Databricks, Amazon Web Services, or Google Cloud Platform; strong experience across multiple platforms is preferred.
Experience with distributed data processing, data pipelines, feature stores, model registries, model serving, APIs, orchestration, and scalable compute environments.
Experience with modern generative AI architectures, including large language models, retrieval-augmented generation, embeddings, vector search, prompt engineering, tool use, function calling, structured outputs, and agent orchestration.
Experience designing or deploying multi-agent AI solutions that coordinate multiple agents, tools, workflows, or decision steps.
Strong knowledge of production engineering practices, including Git, automated testing, CI/CD, containers, APIs, observability, infrastructure as code, and system reliability.
Ability to design secure AI systems using identity and access management, least privilege, secrets management, encryption, private endpoints, network controls, data classification, and audit logging.
Experience communicating technical concepts, model outputs, risks, architecture decisions, and recommendations to nontechnical stakeholders.
Demonstrated ability to work independently, manage ambiguity, influence decisions, and deliver results in a cross-functional environment.
Master’s or Ph.D. in a relevant technical discipline.
Experience with Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Azure Databricks, Databricks Mosaic AI, MLflow, Unity Catalog, Databricks Model Serving, Vector Search, Lakeflow, or Databricks AI Gateway.
Experience with GCP Vertex AI, Gemini, Vertex AI Model Garden, BigQuery, Cloud Storage, Dataflow, Pub/Sub, Cloud Run, GKE, Cloud SQL, Cloud IAM, and Google Cloud Monitoring.
Experience with AWS SageMaker, Amazon Bedrock, S3, Glue, EMR, EKS, Lambda, Step Functions, CloudWatch, or comparable AWS services.
Experience with lakehouse and data mesh architectures using Delta Lake, medallion layers, domain-oriented data products, data contracts, schema enforcement, Unity Catalog, and governed data sharing.
Experience with enterprise data governance and quality tooling, including data catalogs, lineage, access management, data classification, privacy controls, row- and column-level security, and automated data quality validation.
Experience with enterprise AI gateways, model routing, provider abstraction, LLM observability, prompt management, agent evaluation, and multi-model deployment patterns.
Experience with time-series forecasting, optimization, causal inference, simulation, reinforcement learning, recommender systems, NLP, computer vision, or large-scale deep learning.
Experience with Google Workspace, including Google Drive, Docs, Sheets, Slides, Meet, Gmail, and shared collaboration workflows; experience automating or integrating Google Workspace APIs is a plus.
Experience working with Google Cloud migration, modernization, or interoperability initiatives, including hybrid and multi-cloud data and AI architectures.
Publications, patents, open-source contributions, technical presentations, or other evidence of advanced expertise in machine learning or artificial intelligence.
Success will be measured by the ability to consistently convert complex business problems and challenging data into reliable, scalable, secure, and adopted ML and AI solutions. The successful candidate will deliver production systems that create measurable business value, operate effectively across Azure, Databricks, AWS, and GCP environments, and are understood and trusted by both technical and business
stakeholders.
