Overview

Design and lead the implementation of enterprise AI solutions - spanning Classic ML, Generative AI, and Agentic AI - on AWS or Azure, defining AI strategy, architecture patterns, and responsible AI practices for customers.

Responsibilities

• Design end-to-end AI or ML solution architectures on cloud platforms - AWS (SageMaker, Bedrock, Rekognition, Comprehend) or Azure (Azure ML, Azure OpenAI Service, Cognitive Services)
• Define AI strategy and roadmaps aligned with customer business objectives covering Classic ML, GenAI, and Agentic AI
• Architect GenAI solutions using foundation models, RAG patterns, fine-tuning strategies, and multiagent or agentic frameworks
• Design agentic AI architectures including tool use, planning, memory, and orchestration patterns
• Design MLOps pipelines for model training, evaluation, deployment, and monitoring
• Evaluate and recommend AI or ML services, frameworks, and tools for specific use cases
• Lead technical workshops, PoCs, and architecture reviews for AI initiatives
• Collaborate with Data Architects and Data Engineers to ensure data readiness for AI workloads

Qualifications

• 5 - 10 years of experience in AI or ML architecture, data science, or ML engineering
• Deep knowledge of ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Hands-on experience with cloud AI or ML services - AWS (SageMaker, Bedrock) or Azure (Azure ML, Azure OpenAI Service)
• Strong understanding of LLMs, foundation models, prompt engineering, RAG architectures, and agentic AI patterns
• Strong understanding of data preparation, feature engineering, and model evaluation
• Knowledge of responsible AI frameworks and AI governance

Nice to have

• Cloud certification - AWS Machine Learning Specialty or Azure AI Engineer Associate