Consultant, Data & AI Engineer joins the IBM Consulting team to bridge data engineering and applied AI. This role involves building Snowflake Data Cloud foundations, developing ETL/ELT ingestion pipelines, and creating LLM-based applications including RAG systems, agentic workflows, and machine learning models for clients. You will manage data modeling, governance, security, and deployment across cloud environments like AWS, Azure, and GCP. The position requires proficiency in SQL, Python, dbt, Spark, or Snowpark, along with experience in vector stores, embeddings, and orchestration frameworks such as LangChain or LlamaIndex. Key responsibilities include implementing Snowflake Cortex features, managing CI/CD pipelines, and ensuring data quality for AI consumption. You will solve complex business problems by integrating production-grade data solutions into client processes while navigating the technical trade-offs between LLMs, classical machine learning, and rules-based systems.
Build and operate ETL/ELT ingestion pipelines and data warehouse models on Snowflake using structured and unstructured sources.
Develop, evaluate, and deploy LLM-based applications including RAG systems, agentic workflows, and machine learning models.
Prepare, chunk, embed, and govern data for AI consumption while ensuring high quality and proper access control.
Implement solutions using Snowflake Cortex services, Snowpark, and other integrated cloud AI tools.
Establish evaluation metrics, testing protocols, and guardrails to ensure the reliability of AI outputs.
Deploy production-grade solutions across cloud environments using CI/CD, version control, and infrastructure-as-code practices.
Manage data governance, security, and privacy standards including PII handling and role-based access controls.
Translate client requirements into technical solutions while providing clear documentation for handoff and internal reuse.
What we're looking for
Master's Degree (preferred).
Experience in data engineering and applied AI or machine learning, with consulting experience highly desirable.
Proficiency in SQL and Python, including experience with dbt, Spark, or Snowpark.
Experience building ETL/ELT pipelines and data warehouses on cloud platforms like Snowflake, Redshift, BigQuery, or Databricks.
Hands-on experience with LLM applications, including prompt engineering, RAG, vector stores, and orchestration frameworks like LangChain or LlamaIndex.
Proficiency in software engineering practices such as Git, automated testing, CI/CD, and infrastructure-as-code.
Experience with Snowflake Cortex, agentic systems, and AI evaluation tools (preferred).