AI Systems Engineer

Define and execute data science strategy.

Lead AI/ML initiatives in healthcare/insurance.

Design and develop agentic systems for automating workflows.

Translate customer needs into agent behaviors.

Design agentic systems for audit workflows.

Translate customer problems into agent behaviors.

Automate network and systems defense.

Leverage AI for security issue remediation.

Design LLM orchestration frameworks.

Optimize data preprocessing pipelines.

Design LLM orchestration for analyzers.

Develop data pipelines for document processing.

Design and implement LLM orchestration.

Build data pipelines for document processing.

Develop AI agents for customer support.

Translate CX needs into AI solutions.

Evaluate and refine AI-generated content.

Assess accuracy, clarity, and coherence of responses.

Drive the development of GenAI-powered labeling systems.

Identify improvements in quality, speed, coverage, and cost using LLMs.

Gather requirements from internal teams.

Translate requirements into AI solutions.

Build internal content and media systems for architecture and storytelling.

Create automation and repository infrastructure for design assets.

Build and ship full-stack features across the product.

Design and build AI agents and automated workflows.

Lead AI initiatives from concept to production.

Define and implement AI architecture and workflows.

Guide AI services for nonprofits.

Research new AI products.

Design and implement ML-driven reward signals.

Collaborate across teams for integration and testing.

Design and develop intelligent agents using LLMs.

Leverage tool calling and agent orchestration frameworks.

Lead an AI Builder team to develop internal automation tools.

Set the vision and roadmap for AI-driven tooling.

Lead the design of the integration layer connecting ERP systems.

Manage engineering teams focused on data integrations.

Define the technical vision for Feed Relevance.

Coach and develop the ML engineering team.

Promote key information security practices.

Assess and report security risks.

Lead design for trading products from concept to launch.

Collaborate with teams to define UX and product direction.

Promote information security awareness.

Ensure compliance with data protection standards.

Lead cross-platform People & Culture solutions.

Implement AI-driven HR solutions.

Lead design and development of NLU solutions.

Leverage LLMs for text and reasoning tasks.

Redesign developer experience for AI-native work.

Implement AI-native vision with CTO and CISO.

Promote awareness of information security practices.

Drive the development of AI-powered capabilities.

Design and maintain scalable backend services.

Develop evaluation frameworks for GenAI products.

Promote awareness of information security practices.

Define and execute a vision for AI capabilities.

Own the vision and strategy for the Core Platform.

Define the System of Record for inter-team communications.

Promote awareness of information security practices.

Evaluate compliance with data privacy standards.

Lead a high-performing engineering team.

Ensure AI applications are performant and secure.

Enhance user experience through research and data.

Understand and apply relevant platform technologies.

Lead and mentor the Applied AI Solutions Architects team.

Drive AI adoption for digital-native customers.

Develop AI features for user experience.

Collaborate with cross-functional teams.

Support design and development of Generative AI solutions.

Implement core RAG pipelines for AI applications.

Optimize D365 F&O modules for SCM and Finance.

Design and deploy AI workflows.

Develop foundational AI tooling and frameworks.

Facilitate integration of AI features across teams.

Train bandit models and neural networks.

Collaborate with teams on model alignment.

Understand partner workflows and requirements.

Design and deploy scalable agents for internal use.

Architect scalable AI orchestration layers.

Design workflows for agentic systems.

Develop AI orchestration layers for scalable workflows.

Select and configure LLMs based on specific criteria.

Design AI workflows for features.

Integrate LLMs with insurance data.

Scale an AI-native product suite.

Design effective data-to-AI workflows.

Identify AI enablement needs across the organization.

Collaborate to define metrics and tools for improvement.

Develop systems for AI agents and data integration.

Build AI Agent Factory and establish data governance.

Create a reusable media production system.

Establish governance and structure for asset management.

Design and build agentic systems.

Translate customer problems into agent behaviors.

Lead executive LinkedIn marketing programs for top executives.

Manage content and engagement for employee advocacy initiatives.

Lead the development of the Core Platform.

Grow and manage a team of engineers.