Gen AI Architect

We are seeking a hands-on Generative AI Architect to lead the enhancement of our existing virtual assistant, by incorporating state-of-the-art generative AI capabilities. The ideal candidate will design, implement, and optimize solutions that are model, vendor, and platform agnostic.
 

Required Qualifications: 
Technical Expertise:

• Strong programming skills in Java and familiarity with API-driven backend development.
• Experience with AI/ML frameworks like TensorFlow, PyTorch, Hugging Face, or equivalent.
• Proficiency in building and deploying applications on OpenShift or other container orchestration platforms.
• Generative AI Experience: Proven experience in designing and deploying generative AI solutions, including LLM-based applications. ○ Understanding of prompt engineering, fine-tuning, and training generative models.
• System Design and Architecture: ○ Ability to design scalable, fault-tolerant, and secure AI systems.
• Knowledge of data governance, compliance, and model explainability in enterprise environments. 
• Cloud and DevOps: ○ Experience with CI/CD pipelines, containerization, and orchestration tools like Kubernetes.  Familiarity with hybrid cloud and on-premises systems.
• Soft Skills: Strong problem-solving skills and a hands-on approach to tackling technical challenges.
• Excellent communication and collaboration skills to influence diverse technical and non-technical stakeholders.

Key Responsibilities 

• Architectural Leadership: Design and develop an end-to-end architecture for integrating generative AI capabilities into the current virtual assistant.
• Ensure solutions are model-agnostic, vendor-neutral, and adaptable across multiple platforms.
• Lead and mentor the technical team to implement and optimize the architecture. 
• Generative AI Integration:
• Evaluate and select generative AI models and frameworks suitable for conversational AI enhancements.
• Build, fine-tune, and integrate generative models for tasks like response generation, summarization, and personalized interactions.
• Optimize performance, scalability, and accuracy for real-world use cases. 
• Platform Migration and Scalability:
• Ensure seamless integration of backend APIs, maintaining high availability and performance.
• Develop a robust CI/CD pipeline for deploying and managing AI models and services on OCP. 
• Technical Implementation:
• Stay hands-on with coding, prototyping, and testing AI components.
• Collaborate with cross-functional teams, including backend engineers, DevOps, and data scientists, to deliver integrated solutions.
• Build monitoring and alerting systems for AI model performance and application reliability. 
• Collaboration and Stakeholder Management:
• Work closely with product managers, business stakeholders, and engineers to align technical solutions with business goals.
• Provide technical thought leadership, documentation, and knowledge-sharing to support team growth and alignment