Jobs → Data & Analytics → Data Science AI Architect

About the Role We are seeking an experienced Data Science AI Architect to design and lead enterprise-scale AI, Machine Learning, Generative AI, and data science solutions. This role requires strong expertise in AI/ML architecture, data platforms, LLMs, cloud technologies, and productionizing advanced analytics solutions. Key Responsibilities • Design and define end-to-end AI/ML and Data Science architectures for enterprise applications and business use cases • Lead the architecture, development, and deployment of Machine Learning, Deep Learning, Generative AI, and LLM-based solutions • Translate business requirements into scalable AI, data, and analytics architectures • Design AI/ML pipelines covering data ingestion, preprocessing, feature engineering, model development, training, evaluation, deployment, and monitoring • Develop and implement Generative AI solutions using LLMs, RAG, prompt engineering, embeddings, vector databases, and AI agents • Architect scalable data platforms supporting structured, semi-structured, and unstructured data for AI/ML workloads • Collaborate with Data Scientists, ML Engineers, Data Engineers, Software Engineers, and Cloud Architects • Implement responsible AI practices covering security, privacy, explainability, bias detection, model governance, and compliance • Define architecture standards, reusable AI components, APIs, data pipelines, and integration patterns • Conduct technical design reviews, proof-of-concepts, technology evaluations, and architecture assessments • Guide engineering teams in converting AI/ML prototypes into high-performance, scalable production solutions • Monitor AI solution performance, model accuracy, latency, cost, scalability, and reliability Requirements • 10+ years of experience in Data Science, AI/ML, Machine Learning Engineering, or Architecture • Strong experience designing enterprise AI/ML and Data Science architectures • Experience with LLMs, RAG, embeddings, vector databases, prompt engineering, and fine-tuning • Strong understanding of MLOps, model deployment, monitoring, and ML lifecycle management • Experience with data technologies such as Apache Spark, Databricks, Snowflake, data lakes, data warehouses, and streaming platforms • Strong knowledge of REST APIs, microservices, containers, Docker, and Kubernetes • Experience with at least one major cloud platform: AWS, Azure, or GCP • Strong understanding of data governance, security, privacy, and AI governance • Excellent architecture, technical leadership, communication, and stakeholder-management skills