Key Responsibilities Solution Design & Architecture: Design end-to-end technical architectures for applications utilizing generative AI (e.g., LLMs, GANs), intelligent document processing (e.g., OCR, NLP, data extraction), and workflow automation. Define system components, integrations, APIs, and data flows to ensure scalability, performance, and reliability. Collaborate with product managers, developers, and stakeholders to translate business requirements into technical architecture. Document and present architecture, along with pros & cons to senior IT leadership Components of architecture: Generative AI: Create application architecture to integrate generative AI models (e.g., GPT-based models, diffusion models) into applications., including: Foundation Models: Leverage large language models (LLMs) like GPT-4o, LLaMA, Grok, or BERT, and generative adversarial networks (GANs) such as Stable Diffusion or VAE-based systems for text, image, audio, or multimodal generation. Frameworks & Libraries: Utilize Hugging Face Transformers, LangChain, and DeepSpeed Orchestration & Pipelines: Tools like Apache Airflow, Kubeflow, or MLflow Deployment Platforms: Cloud platforms (e.g., AWS, Azure ) Architect solutions for real-time generative AI use cases (e.g., chatbots, content creation, synthetic data generation) and batch processing. Intelligent Document Processing: Architect solutions for automated document ingestion, classification, data extraction, and validation using IDP technologies (e.g., OCR, NLP, computer vision). Ensure seamless integration of IDP systems with existing enterprise platforms (e.g., ERP, CRM). Enhance document processing workflows with AI-driven insights and error reduction. Automation: Develop and implement automation strategies using tools like RPA (Robotic Process Automation), low-code platforms, or custom scripts to streamline repetitive tasks. Integrate automation workflows with AI-driven decision-making systems. Monitor and optimize automated processes for efficiency and adaptability. Integration: Define system components, integrations, APIs, and data flows to ensure scalability, performance, interoperability, and reliability. Design and implement the integration technology stack to connect generative AI, IDP, and automation systems with enterprise ecosystems, including: Middleware & ESB: Utilize tools like Apache Camel, MuleSoft, or Red Hat Fuse for enterprise service bus (ESB) integration API Management: Leverage platforms like Apigee, Kong, or AWS API Gateway for secure and scalable API design and orchestration. Messaging Systems: Implement real-time data exchange with Apache Kafka, RabbitMQ, or Azure Service Bus. Identity & Access Management: Integrate with OAuth 2.0, OpenID Connect, or SAML via tools like Okta or Keycloak for secure system access. Ensure seamless interoperability between cloud, on-premises, and third-party systems. Technical Leadership: Provide technical guidance to development teams during implementation, ensuring adherence to architectural standards. Conduct code reviews, performance tuning, and troubleshooting of complex issues. Collaboration & Innovation: Work closely with cross-functional teams (e.g., data science, DevOps, security) to ensure seamless deployment and operation of solutions. Identify opportunities to leverage emerging technologies to improve existing systems. Contribute to proofs-of-concept (PoCs) and pilot projects to validate new ideas. Compliance & Security: Ensure solutions comply with industry standards, data privacy regulations (e.g., GDPR, CCPA), and security best practices. Design systems with robust authentication, encryption, and auditability features. Job Type: Full-time Pay: $180,000.00 - $190,000.00 per year Work Location: In person