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#20444 - Digital Engineering - Gen AI Architect
$150K - $160K / yearPosted 1 months ago
Full-timeremoteprincipal
Job Description
Key Responsibilities:
• AI Architecture & Solution Design:
Architect enterprise-scale Generative AI solutions leveraging LLMs, embeddings, and retrieval-augmented generation (RAG) pipelines.
Design and implement scalable AI microservices and APIs integrated into existing enterprise ecosystems (Azure, AWS, GCP).
Lead PoCs and pilot implementations for AI-first use cases such as intelligent automation, code generation, knowledge assistants, and predictive analytics.
• Innovation & Technology Leadership:
Define and evolve the organization’s AI architecture strategy, focusing on scalability, security, and ethical AI principles.
Define and design AI Assurance strategy. Define key metrics and approaches to test AI ( Traditional and GenAI ) and work with the platform team to automate the process of capturing and reporting on key metrics.
Introduce emerging Gen AI frameworks (LangChain, LangGraph, CrewAI, OpenAI Agents SDK, etc.) to enable multi-agent orchestration and intelligent workflow automation.
Build reusable AI accelerators, tools, and frameworks to standardize model deployment, evaluation, and governance.
• Customer Engagement & Solutioning:
Partner with clients to identify high-value AI opportunities and translate business problems into technical blueprints.
Lead technical discussions, RFP responses, and client demos showcasing Gen AI capabilities and ROI.
Collaborate with account leaders to shape AI roadmaps and innovation strategies for enterprise clients.
• Delivery Governance & Enablement:
Oversee architecture reviews and ensure best practices in AI model lifecycle management (training, fine-tuning, inference, observability).
Guide engineering and data science teams in adopting AI-first design principles, model operationalization (MLOps/LLMOps), and continuous learning loops.
Ensure compliance with data privacy, responsible AI, and enterprise governance standards.
• Practice & Capability Building:
Build and scale the Generative AI practice across North America — mentoring architects, engineers, and data scientists.
Collaborate with global CoE and platform teams to enhance reusable AI assets, domain-specific models, and evaluation frameworks.
Evangelize AI-first engineering through thought leadership, workshops, and industry events.
________________________________________
• AI Architecture & Solution Design:
Architect enterprise-scale Generative AI solutions leveraging LLMs, embeddings, and retrieval-augmented generation (RAG) pipelines.
Design and implement scalable AI microservices and APIs integrated into existing enterprise ecosystems (Azure, AWS, GCP).
Lead PoCs and pilot implementations for AI-first use cases such as intelligent automation, code generation, knowledge assistants, and predictive analytics.
• Innovation & Technology Leadership:
Define and evolve the organization’s AI architecture strategy, focusing on scalability, security, and ethical AI principles.
Define and design AI Assurance strategy. Define key metrics and approaches to test AI ( Traditional and GenAI ) and work with the platform team to automate the process of capturing and reporting on key metrics.
Introduce emerging Gen AI frameworks (LangChain, LangGraph, CrewAI, OpenAI Agents SDK, etc.) to enable multi-agent orchestration and intelligent workflow automation.
Build reusable AI accelerators, tools, and frameworks to standardize model deployment, evaluation, and governance.
• Customer Engagement & Solutioning:
Partner with clients to identify high-value AI opportunities and translate business problems into technical blueprints.
Lead technical discussions, RFP responses, and client demos showcasing Gen AI capabilities and ROI.
Collaborate with account leaders to shape AI roadmaps and innovation strategies for enterprise clients.
• Delivery Governance & Enablement:
Oversee architecture reviews and ensure best practices in AI model lifecycle management (training, fine-tuning, inference, observability).
Guide engineering and data science teams in adopting AI-first design principles, model operationalization (MLOps/LLMOps), and continuous learning loops.
Ensure compliance with data privacy, responsible AI, and enterprise governance standards.
• Practice & Capability Building:
Build and scale the Generative AI practice across North America — mentoring architects, engineers, and data scientists.
Collaborate with global CoE and platform teams to enhance reusable AI assets, domain-specific models, and evaluation frameworks.
Evangelize AI-first engineering through thought leadership, workshops, and industry events.
________________________________________