Senior AI/ML Operations Engineer

Accountabilities:

• Deploy, promote, and maintain ML and GenAI models, pipelines, and code across environments using established CI/CD infrastructure.
• Develop reusable deployment patterns, tooling, and operational practices that reduce the effort required to launch new AI/ML use cases and client-specific solutions.
• Build and maintain data pipelines supporting classical ML and GenAI workloads, covering ingestion, feature engineering, processing, and serving.
• Operate within Databricks and Snowflake governance frameworks, including access controls and environment boundaries, to support secure and compliant promotion of data, code, and models.
• Independently diagnose and resolve production issues across data pipelines, infrastructure, model-serving systems, and related AI/ML components.
• Automate and monitor production ML inference and feature-engineering workflows, including alerting, incident response, and operational reliability.
• Manage model lifecycles through platforms such as MLflow and Unity Catalog, including experiment tracking, registration, versioning, and controlled promotion across environments.
• Build and maintain infrastructure for retrieval-augmented generation (RAG), including vector search indexing and retrieval pipelines.
• Deploy, host, and maintain MCP servers and tool integrations supporting agentic applications.
• Build evaluation infrastructure for AI systems and collaborate with AI engineering teams on evaluation methodologies and quality standards.
• Implement observability for agents and models through logging, tracing, monitoring, and analysis of production behavior.
• Partner with business stakeholders to understand data and feature requirements and coordinate infrastructure needs with Software Engineering, Data Engineering, Data Science, Security, and DevOps teams.
• Mentor junior engineers on platform practices, operational standards, and reliable AI/ML engineering.
• Contribute to customer-specific implementations when required, including semantic layer configuration and domain-specific analytics initiatives.

Requirements:

• 5+ years of professional experience in AI/ML engineering, MLOps, or a closely related discipline.
• Extensive hands-on AI/ML experience with meaningful depth in either generative AI/agentic systems or classical machine learning, alongside working exposure to the other area.
• Experience with AI/GenAI technologies such as agentic frameworks, RAG systems, vector search, MCP or comparable tool-integration protocols, model-serving or gateway layers, and AI evaluation design.
• Alternatively, strong classical ML experience covering common algorithm families such as XGBoost, gradient boosting, random forests, and neural networks, along with feature engineering, training pipelines, and production deployment.
• Deep hands-on experience with Databricks and/or Snowflake, including AI/ML pipeline development, governance and access-control frameworks such as Unity Catalog, and integration with CI/CD infrastructure.
• Strong experience with model lifecycle and registry platforms such as MLflow, including experiment tracking, model registration, versioning, and promotion across environments.
• Strong proficiency in Python and SQL.
• Demonstrated ability to independently investigate, troubleshoot, and resolve production infrastructure and operational issues.
• Excellent communication skills and the ability to collaborate directly with both technical and non-technical stakeholders.
• A proactive approach to learning new technologies, tools, and frameworks and applying them effectively to real-world projects.
• Experience in healthcare, health insurance, or regulated data environments is a plus.
• Experience building or operating multi-agent systems is a plus.
• Experience with Mosaic AI Gateway or comparable model-serving and gateway platforms is a plus.

Benefits:

• Compensation based on experience, skills, and location, including base salary plus eligibility for performance bonuses and equity grants.
• Unlimited paid time off.
• Work-from-anywhere flexibility.
• Comprehensive health coverage with multiple plan options.
• Equity grants for all employees.
• Growth-focused environment with opportunities for professional development.
• One-time home office setup allowance.
• Monthly cell phone allowance.