Design scalable, secure, and maintainable AI services, APIs, data and feature pipelines, and enterprise application integrations using strong software engineering practices, including source control, code review, automated testing, and technical documentation.
Implement MLOps and GenAIOps practices, including CI/CD, model and prompt versioning, experiment tracking, automated deployment, rollback, and lifecycle management.
Establish production monitoring and evaluation to track model performance, data and prediction drift, groundedness, bias, data quality, reliability, safety, usage, cost, and operational health, with appropriate alerting and retraining or remediation workflows.
Implement Responsible AI, security, and governance controls, including risk assessments, human-in-the-loop oversight, access controls, data protection, traceability, and safeguards against hallucination, prompt injection, and data leakage.
Troubleshoot production AI solutions, perform root-cause analysis, and continuously improve reliability, scalability, performance, and operational supportability.
Translate business opportunities into well-defined AI problem statements, success measures, solution designs, and delivery plans, collaborating with Business, Data Science and Engineering, Architecture, Security, Legal, Compliance, and Governance teams to deliver production-ready solutions aligned with enterprise standards.
What Do You Need?
To be considered for this role, an individual should meet the following minimal requirements:
Bachelor’s degree in Data Science, Machine Learning, Statistics, Computer Science, Data Mining, Applied Mathematics, Engineering, or another quantitative or technical discipline.
6+ years of hands-on experience in Machine Learning, Data Science, AI Engineering, and/or advanced analytics.
Hands-on experience developing and deploying machine learning solutions, including model development, validation, evaluation, optimization, and production monitoring.
Experience building Generative AI or agentic AI applications using technologies and patterns such as RAG, vector search, embeddings, tool calling, structured outputs, and AI evaluation frameworks.
Proficiency in one or more programming and analytical technologies such as Python, R, Spark, Scala, and SQL.
Strong understanding of supervised and unsupervised machine learning techniques, including neural networks, XGBoost, support vector machines, gradient boosting, random forests, and related algorithms.
Experience working with both structured and unstructured data, including feature engineering, information retrieval, dimensionality reduction, and feature extraction.
Demonstrated problem-solving skills with the ability to work independently while collaborating effectively across technical and business teams.
Preferred Qualifications
Master’s or PhD in Data Science, Machine Learning, Statistics, Computer Science, Data Mining, Applied Mathematics, Engineering, or another quantitative discipline.
Experience with advanced analytics, predictive modeling, machine learning, statistical techniques, and data visualization.
Advanced proficiency with Python/R, Spark, SQL, MLOps, GenAIOps, and agentic AI workflows.
Experience designing and operating production Generative AI solutions utilizing RAG, embeddings, vector and semantic search, tool calling, orchestration, structured outputs, and AI evaluation frameworks.
Experience with Snowflake Cortex, Microsoft Foundry, Azure ML, Azure AI Search, OpenAI Agents SDK, Claude Agents SDK, Databricks, or comparable AI/ML platforms.
Experience with Snowflake, Microsoft Fabric, dbt, Azure DevOps, or comparable enterprise platforms.
Experience with MLflow or comparable experiment tracking and model registry capabilities, containerization technologies such as Docker/Kubernetes, and infrastructure-as-code practices.
Knowledge of enterprise identity and access management, privacy, data governance, model risk management, security, and Responsible AI controls.
Experience operating production AI/ML solutions with model observability, service-level monitoring, cost controls, incident management, usage telemetry, and continuous performance improvement.
