Highlights

This individual will define the target AI architecture, evaluate technology and model Eligible to workions, establish reusable architectural patterns, and guide engineering teams through implementation. Develop strategies for model routing, token Eligible to workimization, caching, prompt Eligible to workimization, and inference-cost management.

Numbers & Facts

LocationPA

Description

Job Title: - Senior AI Architect

Location: Oaks, PA

Rate:- $77/ HR

ROLE_DESCRIPTION -

Role Summary

The Senior AI Architect will provide overall architectural leadership for enterprise AI solutions. This individual will define the target AI architecture, evaluate technology and model Eligible to workions, establish reusable architectural patterns, and guide engineering teams through implementation.

Key Responsibilities

  • Define end-to-end architecture for enterprise AI and GenAI solutions.
  • Establish reference architectures and reusable patterns for LLM, SLM, RAG, agentic AI, and traditional ML solutions.
  • Evaluate AI models and platforms based on business requirements, accuracy, latency, security, scalability, and cost.
  • Define criteria for determining when to use AI, what type of AI to use, and when traditional technology or automation is more appropriate.
  • Develop strategies for model routing, token Eligible to workimization, caching, prompt Eligible to workimization, and inference-cost management.
  • Architect secure integration of AI solutions with enterprise applications, APIs, data platforms, and knowledge repositories.
  • Provide architecture guidance for AI agents, orchestration, vector databases, embeddings, APIs, and model gateways.
  • Collaborate with security, data, infrastructure, governance, and application architecture teams.
  • Provide technical leadership to engineering teams and conduct architecture/design reviews.
  • Help translate business use cases into scalable AI solution architectures.
  • Required Skills
  • Strong enterprise solution architecture experience with significant exposure to AI/ML and GenAI.
  • Hands-on understanding of LLMs, SLMs, RAG, embeddings, vector databases, AI agents, prompt engineering, and model evaluation.
  • Experience with cloud AI platforms such as Azure AI, AWS, or Google Cloud.
  • Understanding of commercial and open-source AI models.
  • Strong knowledge of APIs, microservices, containers, cloud architecture, security, and enterprise integration.
  • Ability to evaluate architectural trade-offs across quality, performance, risk, scalability, and cost.

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