AIEngineer–Python&Snowflake

## Accountabilities Design, build, and maintain AI-powered data platforms, pipelines, and data products supporting advanced analytics and machine learning use cases. Develop scalable backend systems and data workflows using Python and Snowflake, ensuring performance and reliability. Prepare, transform, and manage large-scale structured and unstructured datasets for AI, ML, and Generative AI applications. Build and optimize feature engineering frameworks, embeddings, vectorized datasets, and semantic data models. Deploy, monitor, and maintain AI/ML models in production environments, ensuring stability, scalability, and observability. Develop APIs, microservices, and backend services to expose AI capabilities across enterprise systems. Collaborate with cross-functional teams to operationalize AI solutions using MLOps and LLMOps best practices. Optimize Snowflake architecture for performance, cost efficiency, and large-scale AI workloads. Support Retrieval-Augmented Generation (RAG) pipelines and other Generative AI architectures. Evaluate and implement emerging AI technologies, including LLMs, vector databases, AI agents, and automation frameworks. Contribute to architectural decisions and technical strategy for AI-driven systems and platforms. Ensure strong standards of data quality, governance, security, and system observability across solutions. Requirements The ideal candidate brings 3–7 years of experience in AI engineering, data engineering, or machine learning engineering, with strong hands-on expertise in building production-grade systems. You should be highly proficient in Python and experienced in Snowflake-based data solutions, with strong SQL and data modeling capabilities. Strong experience in Python for building scalable applications, data pipelines, and backend services. Hands-on expertise with Snowflake, including data modeling, performance optimization, and architecture design. Advanced SQL skills and solid understanding of data architecture and datab