Gen AI Architect
Listed on 2026-07-01
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IT/Tech
AI Engineer (Applied/Software), Cloud Computing: Infrastructure & Operations, Machine Learning/ ML Engineer, Systems Engineer
Job Title
Technical Expertise:
Strong programming skills in Java and familiarity with API-driven backend development. Experience with AI/ML frameworks like Tensor Flow, PyTorch, Hugging Face, or equivalent. Proficiency in building and deploying applications on Open Shift 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 Dev Ops:
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.
1. 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.
2. 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.
3. 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.
4. Technical Implementation:
Stay hands-on with coding, prototyping, and testing AI components. Collaborate with cross-functional teams, including backend engineers, Dev Ops, and data scientists, to deliver integrated solutions. Build monitoring and alerting systems for AI model performance and application reliability.
5. 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.
Preferred Qualifications
Experience working with virtual assistant platforms like Google Dialogflow or similar.
Knowledge of multiple generative AI models, including proprietary and open-source solutions.
Understanding of NLP, conversational AI, and related frameworks.
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