AI Application Architect

Key Responsibilities

• Architect and design application and system architectures for ML/GenAI solutions on cloud platforms, tools (GCP, Vertex AI, IBM Watsonx).
• Build, deploy, and maintain enterprise-scale AI/ML applications, ensuring they are production-ready, secure, and scalable.
• Lead research and adoption of emerging AI/GenAI technologies, frameworks, and tools at the enterprise level.
• Partner with product, engineering, and business teams to translate business challenges into robust AI/GenAI solutions.
• Define end-to-end architecture for data pipelines, model development, deployment, monitoring, and retraining.
• Promote and evangelize best practices in AI/ML system design, data governance, MLOps, and ethical/responsible AI.
• Provide technical leadership and mentorship to AI/ML engineers and data scientists across business units.
• Stay current with advances in AI/ML, GenAI, LLMs, and cloud-native architecture, and identify opportunities for adoption.
• Define roadmaps and strategies that accelerate AI adoption and help the enterprise transition from descriptive to predictive and prescriptive analytics.
• Collaborate with executives and cross-functional leaders to advocate AI/GenAI capabilities and drive enterprise-wide adoption.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.

Experience

• 7+ years of experience designing, building, and deploying AI/ML solutions including monitoring, scaling, and optimization in production at enterprise scale.
• Hands-on expertise in cloud platforms (GCP Vertex AI (preferred), AWS SageMaker, or Azure ML) and cloud-native application design.
• Proven experience designing enterprise-grade application architectures for ML/GenAI workloads.
• Strong programming skills in Python, Java, or C++, and proficiency with ML/AI frameworks (PyTorch, TensorFlow, Keras).
• Deep understanding of ML/AI fundamentals: statistical modeling, architectures, representation, reasoning, and generative models (LLMs, diffusion).
• Hands on experience in one or more ML domains such as NLP, computer vision, or reinforcement learning.
• Hands on experience in MLOps practices and tools (MLflow, TFX, Kubeflow, CI/CD pipelines, model monitoring).
• Strong knowledge of software architecture principles, cloud networking, and security best practices.
• Excellent communication and leadership skills, with the ability to influence stakeholders and lead technical discussions across business and engineering teams.