Job opening for Snowflake Architect AI hexaware technologies

Senior-level role (8+ years) for a Snowflake-focused data/ML engineer with strong AI experience and solid Python skills. The position centers on designing and operating Snowflake data platforms, building data pipelines and MLOps workflows, and delivering AI/ML solutions into production. Job Title Senior Snowflake + AI Engineer (8+ years ) Experience - Minimum 8+ years of professional experience in data engineering, ML engineering, or a related software engineering role, with demonstrable experience on Snowflake, applied AI/ML, and Python. Role Summary We are looking for a Senior Snowflake + AI Engineer to lead the design, implementation and optimization of our Snowflake data platform and end-to-end AI/ML solutions. The ideal candidate will combine deep Snowflake expertise with hands-on AI/ML experience and production-grade Python skills to build scalable data pipelines, model training and deployment workflows, and ensure governed, performant data access for analytics and ML use cases. Key Responsibilities - Lead architecture and implementation of Snowflake-based data warehouses and data lakes (schema design, data models, performance tuning). - Design and build reliable, scalable ETL/ELT pipelines feeding Snowflake (batch and streaming where applicable). - Implement Snowflake-specific features: Snowpipe, Streams & Tasks, Time Travel, cloning, resource monitors, secure data sharing, role-based access. - Partner with data scientists and ML engineers to operationalize AI/ML workflows: data preparation, feature engineering, model training, validation, and deployment. - Build and maintain MLOps pipelines for model reproducibility, CI/CD, automated testing, monitoring and retraining. - Write production-quality Python code for data transformation, feature engineering, model development and inference. - Optimize query performance, storage costs and warehouse sizing; implement best practices for concurrency and workload isolation. - Ensure data governance, lineage, security and compliance standards are met. - Mentor junior engineers, lead technical design reviews, and drive adoption of best practices (testing, observability, documentation). Required (Must-Have) Skills - Snowflake (expert): architecture, schema design, performance tuning, Snowpipe, Streams & Tasks, security and governance. - AI / ML (strong): practical experience with model development, evaluation, and production deployment (supervised learning, NLP/LLM exposure preferred). - Python (solid): proficiency in pandas, NumPy, and frameworks used for ML and production deployment (scikit-learn, PyTorch or TensorFlow, Hugging Face experience is a plus). - Data engineering fundamentals: ETL/ELT design patterns, streaming concepts, data modelling (star/snowflake schemas), data quality and testing. - MLOps & deployment: experience with CI/CD for models, containerization (Docker), model serving, monitoring and model versioning. - SQL: advanced SQL skills for complex transformations and query optimization in Snowflake. - Cloud platforms: hands-on experience with at least one cloud provider (AWS / Azure / GCP) and familiarity with their integration with Snowflake. - Collaboration & communication: ability to translate technical trade-offs for stakeholders and lead cross-functional projects. Nice-to-Have - Experience with dbt for transformations on Snowflake. - Familiarity with Snowflake ecosystem features (Data Marketplace, Secure Views, External Functions). - Experience with orchestration tools (Airflow, Prefect, Dagster). - Experience with vector databases, embeddings, and LLM integration patterns. - Knowledge of data privacy frameworks and regulatory compliance (GDPR, HIPAA as applicable).