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Architect
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Architect
Virtusa- Posted 2 hours ago
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Job Description
AI Engineer – Agentic AI
Design, develop, and deploy advanced Generative AI and Agentic AI solutions end-to-end.
Bring hands-on expertise across Traditional AI, Generative AI, and Agentic AI workloads.
Build and deploy AI solutions natively on AWS Bedrock.
Integrate and optimize LLMs for enterprise AI use cases.
Design and implement sophisticated agent orchestration workflows.
Apply advanced prompt engineering techniques to improve accuracy, relevance, and task completion.
Design and implement complex Retrieval-Augmented Generation (RAG) pipelines.
Implement agent memory and state management to support multi-turn interactions and context continuity.
Design and implement AI guardrails for safety, compliance, responsible AI, and hallucination mitigation.
Develop production-grade AI solutions with a focus on scalability, reliability, performance, and maintainability.
Collaborate with architecture, engineering, and business teams to translate AI use cases into production-ready solutions.
AI Integration Lead
Provide technical leadership and architectural vision across the enterprise AI ecosystem.
Bring deep expertise across Traditional AI, Generative AI, and Agentic AI, with strong hands-on experience with AWS Bedrock.
Define and drive the enterprise AI integration architecture across AI platforms, applications, and enterprise systems.
Integrate AI services with the broader CAT ecosystem through scalable APIs, microservices, and custom connectors.
Design and implement integrations with critical enterprise platforms such as CAT Tracker, Verisk, and SharePoint.
Architect scalable, secure, highly available AI microservices and APIs.
Establish enterprise-wide AI observability, monitoring, logging, and performance management.
Define and enforce security, access control, data protection, and compliance standards for AI solutions.
Establish DevOps and CI/CD practices tailored to AI/ML and GenAI deployments.
Drive productionization of AI solutions across development, testing, deployment, and operational environments.
Ensure seamless interoperability between AI engineering platforms and enterprise applications.
Act as the bridge between AI engineering, enterprise architecture, integration, security, and business teams.
Ensure AI solutions are scalable, secure, resilient, highly available, and production-ready.
Design, develop, and deploy advanced Generative AI and Agentic AI solutions end-to-end.
Bring hands-on expertise across Traditional AI, Generative AI, and Agentic AI workloads.
Build and deploy AI solutions natively on AWS Bedrock.
Integrate and optimize LLMs for enterprise AI use cases.
Design and implement sophisticated agent orchestration workflows.
Apply advanced prompt engineering techniques to improve accuracy, relevance, and task completion.
Design and implement complex Retrieval-Augmented Generation (RAG) pipelines.
Implement agent memory and state management to support multi-turn interactions and context continuity.
Design and implement AI guardrails for safety, compliance, responsible AI, and hallucination mitigation.
Develop production-grade AI solutions with a focus on scalability, reliability, performance, and maintainability.
Collaborate with architecture, engineering, and business teams to translate AI use cases into production-ready solutions.
AI Integration Lead
Provide technical leadership and architectural vision across the enterprise AI ecosystem.
Bring deep expertise across Traditional AI, Generative AI, and Agentic AI, with strong hands-on experience with AWS Bedrock.
Define and drive the enterprise AI integration architecture across AI platforms, applications, and enterprise systems.
Integrate AI services with the broader CAT ecosystem through scalable APIs, microservices, and custom connectors.
Design and implement integrations with critical enterprise platforms such as CAT Tracker, Verisk, and SharePoint.
Architect scalable, secure, highly available AI microservices and APIs.
Establish enterprise-wide AI observability, monitoring, logging, and performance management.
Define and enforce security, access control, data protection, and compliance standards for AI solutions.
Establish DevOps and CI/CD practices tailored to AI/ML and GenAI deployments.
Drive productionization of AI solutions across development, testing, deployment, and operational environments.
Ensure seamless interoperability between AI engineering platforms and enterprise applications.
Act as the bridge between AI engineering, enterprise architecture, integration, security, and business teams.
Ensure AI solutions are scalable, secure, resilient, highly available, and production-ready.
More Info
Key Skills
AWS Bedrock
AI guardrails
compliance standards
Generative AI
LLMs
agent memory and state management
prompt engineering
custom connectors
Traditional AI
security access control
Agentic AI
agent orchestration workflows
AI observability
About Company
