What are the responsibilities and job description for the Gen AI Architect || W2 || Hybrid position at Cloud Bridge Solutions?
Job Details
Gen AI Architect
Charlotte, NC (Need Onsite day 1, hybrid 3 days from office).
Contract – w2
Job Description:
The Gen AI Architect will leverage advanced Generative AI models and Azure OpenAI services to develop innovative solutions for investment banking processes. The ideal candidate will have a strong background in investment banking, hands-on experience with Microsoft Azure OpenAI, and expertise in Retrieval-Augmented Generation (RAG).
Responsibilities:
- Define and drive the AI/ML architecture and roadmap, including both traditional machine learning and Generative AI (GenAI) use cases.
- Design comprehensive end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring frameworks.
- Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks, utilizing tools such as LangChain, LangGraph, or similar.
- Collaborate with business stakeholders to translate requirements into scalable AI-driven technical solutions.
- Evaluate and select appropriate AI/ML tools, cloud services, frameworks, and libraries based on use case needs and industry best practices.
- Ensure models adhere to governance, security, explainability, and regulatory compliance, embedding ethical AI principles into system design.
- Guide engineering teams in the implementation of AI components, emphasizing scalability, reliability, and performance optimization.
- Partner with DevOps teams to establish CI/CD pipelines for AI, including model versioning, deployment automation, and ongoing A/B testing.
- Keep abreast of the latest industry research, breakthroughs, and emerging trends in AI, recommending adoption of best practices and innovative solutions.
Requirements:
- Proven experience 10 years, excel in leading AI/ML architecture and strategy in enterprise environments.
- Strong expertise in designing and deploying large-scale AI/ML solutions, including LLMs and RAG frameworks.
- Experience with AI/ML tools and frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, or similar.
- Deep understanding of data workflows, feature engineering, model training, evaluation, and deployment.
- Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform) and services tailored for AI deployment.
- Familiarity with model governance, security, explainability, and ethical AI standards.
- Experience in developing CI/CD pipelines for AI/ML, including model versioning, monitoring, and performance tuning.
- Strong problem-solving, communication, and stakeholder management skills.
- Preferred, but not required:
- Advanced degree (Ph.D., Master’s) in Computer Science, Data Science, AI, or related fields.
- Publications or practical contributions to AI research and open-source projects.
- Experience working in regulated industries or environments requiring compliance and governance.
- Familiar with project management and Agile practices.
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