Senior AI Architect

• 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.
• 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 optimization, caching, prompt optimization, 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.

• 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.
• 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 optimization, caching, prompt optimization, 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.

• 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 optimization, caching, prompt optimization, 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.

Discretionary Annual Incentive.

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