AI and GenAI Solution Architect

About the Role:

AI Solution Architect

Responsibilities:

• Lead from the front as a hands-on subject matter expert, architecting and crafting scalable AI and Generative AI solutions.
• Partner with customers to deeply understand their business needs and pain points related to AI/ML and Generative AI.
• Lead technical responses to RFPs (Request for Proposals), crafting detailed solution architectures, technical approaches, and implementation methodologies
• Design and build robust and secure solution architectures, encompassing conceptual, technical, and security considerations. This includes high-level and low-level designs, implementation roadmaps, and integration plans with existing workflows.
• Develop compelling solution proposals, including high-level approach and architecture diagrams with clear narratives that explain key design choices, such as:
• Selection of appropriate AI/ML models (e.g., supervised learning, unsupervised learning, deep learning) based on data characteristics and problem requirements.
• Strategies for data pre-processing, feature engineering, and model training.
• Considerations for model explainability, bias mitigation, and fairness.
• Work along with engineering team to create proof-of-concept (POC) demonstrations that validate the proposed solutions, considering factors like:
• Identify and leverage existing AI/ML and Generative AI accelerators and solutions to enhance presales activities, client demos, and POC development.
• Continuously stay updated on the latest advancements in Generative AI through ongoing learning and training initiatives.

Qualifications:

• Bachelors or masters degree in computer science, Engineering, or a related field.
• 10+ years of experience in designing and developing AI/ML and Generative AI solutions.
• Proven track record of successfully translating customer requirements into effective AI-powered solutions.
• Strong understanding of machine learning principles, frameworks (e.g., pandas, TensorFlow, PyTorch), and best practices.
• Expertise in cloud computing platforms (Azure, AWS, GCP) for AI/ML deployments.
• In-depth knowledge of Generative AI models and services like Azure OpenAI, Google AI, AWS, Anthropic, Nvidia NeMo, and Microsoft Copilot etc., plus open source LLMs
• Experience with natural language processing (NLP) techniques for tasks like text classification, sentiment analysis, and machine translation.
• Understanding of computer vision (CV) concepts and frameworks for tasks like object detection, image recognition, and image segmentation; and reinforcement learning (RL).
• Familiarity with designing applications with Nvidia CUDA, and familiarity with NVIDIA NeMo framework for GenAI application development.
• Knowledge of model deployment strategies, including containerization and orchestration tools (e.g., Docker, Kubernetes).