Consultant, Data & AI Engineer

IBM Consulting helps organizations advance their hybrid cloud and AI journeys through collaborative, technology-driven solutions. The Consultant, Data & AI Engineer will build Snowflake-based data foundations and develop, deploy, and govern LLM and machine learning applications for clients, while collaborating with architects, analysts, and data scientists in a client-facing consulting environment.

Responsibilities:

• Data Pipeline Development: Design, build, and operate ETL / ELT ingestion pipelines and data warehouse / lakehouse models on Snowflake, including structured and unstructured sources feeding downstream analytics and AI use cases
• AI Solution Development: Build, evaluate, and deploy LLM-based applications (RAG, agents, document intelligence, conversational interfaces) and classical ML models that solve concrete client business problems
• AI-Ready Data Engineering: Prepare, chunk, embed, and govern data for AI consumption, including vector search, feature pipelines, and semantic layers, with attention to data quality, lineage, and access control
• Snowflake AI Platform: Implement solutions using Snowflake Cortex (LLM functions, Cortex Search, Cortex Analyst, Cortex Agents), Snowpark, and related services, and integrate with external model providers and cloud AI services where appropriate
• Evaluation and Reliability: Define evaluation datasets and metrics for AI outputs, implement testing, observability, and guardrails, and iterate on prompts, retrieval, and model selection based on measured results
• Implementation and Deployment: Deploy solutions to cloud environments (AWS, Azure, GCP, Snowflake) with CI/CD, version control, and infrastructure-as-code practices, ensuring scalability, cost efficiency, and performance
• Client Engagement: Understand client requirements, present technical options and trade-offs, and work with client teams to integrate data and AI solutions into their business processes
• Governance and Security: Apply data governance, privacy, and responsible AI practices, including PII handling, role-based access, and auditability, across both data and AI components
• Documentation and Knowledge Transfer: Produce clear documentation for pipelines, models, prompts, and architectures to enable client handoff and internal reuse
• Continuous Learning: Track developments in the Snowflake and AI ecosystems and bring practical recommendations to engagements and to the practice

Requirements:

• • Spanning data engineering (data management, database development, ETL / ELT, data warehousing) and applied AI or machine learning; consulting or professional services experience is highly desirable
• • Strong SQL and Python; experience with dbt, Spark, or Snowpark for transformation
• • Experience building ETL / ELT ingestion pipelines and developing data warehouses on a cloud data platform (Snowflake preferred; Redshift, BigQuery, Databricks also relevant)
• • Hands-on experience building LLM-based applications: prompt engineering, retrieval-augmented generation, vector stores and embeddings, and orchestration frameworks (for example LangChain, LlamaIndex, or equivalent)
• • Working knowledge of ML fundamentals (supervised and unsupervised learning, model evaluation) and of the trade-offs between LLM, classical ML, and rules-based approaches
• • Proficiency with cloud platform services for data and AI workloads (AWS, Azure, or GCP), including managed database and data processing services
• • Experience with software engineering practices: Git-based version control, automated testing, CI/CD, and code review
• • Understanding of data governance, security, and compliance best practices as applied to both data pipelines and AI systems
• • Strong communication skills with the ability to explain data and AI concepts to non-technical stakeholders and build client relationships
• • Ability to work in cross-functional, Agile teams in a dynamic environment
• This job can be performed from anywhere in the US
• Master's Degree
• • Snowflake Cortex Experience: Hands-on with Cortex LLM functions, Cortex Search, Cortex Analyst, Cortex Agents, or Snowpark Container Services; SnowPro certification a plus
• • Agentic Systems: Experience building multi-step or tool-using agents, including Model Context Protocol (MCP) integrations, and evaluating agent reliability
• • AI Evaluation and Observability: Experience with LLM evaluation frameworks, tracing, and monitoring in production
• • Ingestion Tooling: Fivetran, Matillion, Openflow / NiFi, or similar; streaming and real-time ingestion patterns
• • AI-Assisted Development: Proficiency with AI coding tools (for example Claude Code, GitHub Copilot, Cursor) to accelerate delivery while maintaining code quality
• • Industry Context: Exposure to AI and data applications in healthcare, financial services, supply chain, or retail / CPG
• • Additional Languages: Scala or JavaScript