Lead enterprise AI architecture for LPL Financial’s AI portfolio, defining governance, security, and operational standards across the AI product lifecycle.
Compensation
Pay Range: $155,942.00 - $259,869.00
Responsibilities
Define and drive architecture for enterprise AI portfolio
Establish governance and engineering patterns for AI product development lifecycle
Architect AI solution and platform architecture for user-facing workspaces and agent runtimes
Define patterns for durable user work and integration into existing web platforms
Architect intent routing, context assembly, and delegation between agents and domain services
Requirements
Bachelor’s or Master’s degree in Computer Science, Engineering, AI, or a related field
Nice to have: AWS (EKS, Amazon Bedrock, API Gateway, IAM, PostgreSQL, DynamoDB, S3)
Nice to have: AI gateways and OpenAI-compatible inference APIs
Nice to have: Agent protocols and frameworks (tool calling, MCP, A2A, multi-agent orchestration, embeddings, vector search)
Nice to have: Financial services or highly regulated domains experience
Defines and drives architecture for the enterprise AI portfolio, including advisor-facing workspaces, centralized AI control plane, and agentic solutions.
Locations (2)
Fort Mill, South Carolina, United States
Charlotte, North Carolina, United States
Compensation
Pay Range: $155,942.00 - $259,869.00
Responsibilities
Own and evolve reference architectures for AI-enabled applications, including user-facing AI workspaces and the agent runtime, data stores, and services.
Define patterns for durable user work: conversational and non-conversational sessions, memory and artifact continuity, graceful degradation, and integration into existing web platforms.
Architect intent routing, context assembly, and delegation between agents and domain services.
Define how AI products conform to a governed perimeter: API/SDK integration, AI gateway routing, policy enforcement, managed model inference, audit logging, and cost attribution.
Senior Cybersecurity Engineer responsible for deploying, managing, and maintaining security tools, ensuring they meet evolving business and security requirements, and developing KPIs to measure effectiveness across Danaher’s global cybersecurity program.
Responsibilities
Design, develop, and maintain the CrowdStrike Falcon platform and all associated modules, including Falcon, FEM, and Identity.
Collaborate efficiently with Danaher cross‑functional teams to ensure seamless integration and optimal functionality.
Implement new features and enhance existing functionalities to improve platform performance and security.
Address complex technical challenges by delivering robust and scalable solutions.
Partner with stakeholders to uphold Zero Trust security principles, securing identity, device, and access management.
Requirements
Bachelor’s degree in computer science, Information Technology, or related field.
Nice to have: Bachelor’s degree in computer science, Information Technology, or related field.
Nice to have: Relevant certifications such as CISSP, CISM, or GIAC are a plus.
Define and lead the reference architecture for third-party AI products within IT, enabling secure, scalable agentic capabilities and accelerating enterprise-wide AI adoption.
Compensation
The salary range for this position is $185,000 - $220,000 per year. Final compensation will be determined based on several factors, including but not limited to skills, relevant experience, and work location. Please note this range reflects base salary and does not include incentive compensation or potential equity grants. We also offer a comprehensive and highly competitive benefits package.
Responsibilities
Own the architectural vision, principles, and guardrails for AI-first capabilities, including agent orchestration, runtime hosting, model gateways, retrieval/grounding, and enterprise integrations.
Define reference architectures for agentic runtimes, tool integration, policy enforcement, identity, and secure data access in production.
Drive non-functional requirements (reliability, performance, cost efficiency, scalability) and establish SLOs and validation approaches.
Translate business and operational requirements into scalable AI flow architectures that are grounded in customer context and AI best practices.
Develop and optimize data ingestion, transformation, and orchestration pipelines across diverse enterprise systems.
Requirements
Nice to have: AI/ML or GenAI workloads
Nice to have: vector databases
Nice to have: embeddings
Nice to have: feature engineering pipelines
Nice to have: event-driven or real-time data architectures
