AI Architect

Primary Skills

• Hypothesis Testing
• T-Test, Z-Test
• Regression (Linear, Logistic)
• Python/PySpark
• SAS/SPSS
• Statistical analysis and computing
• Probabilistic Graph Models
• Great Expectation
• Evidently AI
• Forecasting (Exponential Smoothing, ARIMA, ARIMAX)
• Tools (KubeFlow, BentoML)
• Classification (Decision Trees, SVM)
• ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet)
• Distance (Hamming Distance, Euclidean Distance, Manhattan Distance)
• R/ R Studio
• Data Science
• Advanced
• Senior AI/ML Engineer

Job requirements

• Experience in Data Science, Machine Learning, Artificial Intelligence, or related fields, with strong proficiency in SQL and Python.
• (preferably SQL)
• Hands-on experience in data analysis, feature engineering, model development, evaluation, and deployment of machine learning solutions to solve business problems.
• Good understanding of Generative AI concepts, including Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and emerging AI frameworks and protocols such as MCP.
• Ability to work with structured and unstructured data, build scalable AI/ML solutions, and collaborate with cross-functional teams to translate business requirements into data-driven outcomes.
• Strong problem-solving, analytical, and communication skills, with a demonstrated willingness and enthusiasm to learn new technologies, tools, and emerging AI/ML trends in a fast-evolving landscape.

Preferred Qualifications

• Experience with Snowflake data platform; exposure to Snowflake AI capabilities and Cortex services will be an added advantage.
• Familiarity with MLOps, model deployment, monitoring, and AI application lifecycle management.
• Knowledge of data warehousing, data engineering concepts, and API integrations.
• Exposure to GenAI application development, vector databases, semantic search, and AI orchestration frameworks.