Want to know if you’re a fit? Upload your resume and let our AI show you.
Skills
Access Controlunmatched
Amazon Web Services (AWS)unmatched
Application Integrationunmatched
Application Programming Interface (API)unmatched
Architectural Designunmatched
Artificial Intelligence (AI)unmatched
Blueprintsunmatched
Business Caseunmatched
Computer Systemsunmatched
Continuous Deployment/Deliveryunmatched
Continuous Integrationunmatched
DevOpsunmatched
Dockerunmatched
GCP (Good Clinical Practices)unmatched
Gitunmatched
Input/Outputunmatched
Knowledge Managementunmatched
Microsoft Windows Azureunmatched
Performance Reviewsunmatched
Privacy Controlsunmatched
Prototypingunmatched
Python Programming/Scripting Languageunmatched
Requirements Managementunmatched
Right-Sizingunmatched
Shallow Parsingunmatched
Systems Analysisunmatched
Technical Leadershipunmatched
Test Automationunmatched
Threat Modelingunmatched
Traffic Shapingunmatched
Use Casesunmatched
Description
Job Duty Descriptions
Lead architecture for GenAI/LLM solutions using patterns such as RAG, tool/function calling, and agentic workflows.
Design secure enterprise knowledge integration and define data access/segregation controls.
Establish Responsible AI, privacy, and security controls for GenAI.
Provide technical leadership across stakeholders; document architecture decisions, risks, and roadmaps; guide teams on performance and cost optimization.
Define AI/ML target architecture and reference patterns aligned to business strategy, enterprise standards, and regulatory requirements.
Translate business use cases into end-to-end solution designs across data, ML, application, integration, and infrastructure layers.
Establish technical guardrails (security, privacy, scalability, performance, observability) for AI/ML platforms and workloads.
Review and approve architecture/designs; document decisions, trade-offs, and standards; mentor teams on best practices.
Design and govern the AI/ML platform and MLOps lifecycle (data ingestion, training, validation, deployment, monitoring, retraining).
Define CI/CD standards for ML (model versioning, reproducible pipelines, automated testing, approvals, rollback).