Introduction
A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
Your Role And Responsibilities
We are looking for a Consultant, AI and Data Engineer to join our growing team of experts. This role sits at the intersection of data engineering and applied AI: you will build the Snowflake Data Cloud foundations that enterprise AI depends on, and you will build the AI solutions that run on top of them. The work spans data ingestion pipelines, data modeling and architecture, data governance and security, and the design, development, and deployment of LLM-based and machine learning applications for clients.
The ideal candidate is a strong pipeline builder who is equally comfortable preparing and modeling data for analytics as they are engineering retrieval-augmented generation (RAG) systems, agentic workflows, and model-serving pipelines. You understand that AI solutions are only as good as the data behind them and you take ownership of both halves. You will work closely with solution architects, data analysts, and data scientists to deliver production-grade solutions across ongoing customer projects.
This position demands a self-directed individual comfortable working across the diverse data and AI needs of multiple teams, systems, and products in a client-facing, fast-paced 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.
This job can be performed from anywhere in the US.
Preferred Education
Master's Degree
Required Technical And Professional Expertise
- 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.
Preferred Technical And Professional Experience
- 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.