Consultant, Data & AI Engineer

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. The posting includes introduction, role and responsibilities, responsibilities list, preferred education, required technical and professional expertise, preferred technical and professional experience. No explicit Benefits, Pay, Schedule. So we should not include those sections. We need headings: maybe "About the role" (intro), "Responsibilities", "Requirements" (maybe combine required expertise), "Qualifications" (preferred education?), "Skills" (maybe technical skills). But we must not create sections with filler. We need to decide which sections have substantive content. We have: - Introduction: description of IBM Consulting, culture, etc. Could be "About the role" or maybe "About the role". The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can use About the role for intro. - Responsibilities: we have a list of responsibilities bullet points. - Requirements: could be "Required Technical And Professional Expertise". That's required. - Qualifications: could be "Preferred Education" and maybe "Preferred Technical And Professional Experience". But we need to decide if we combine into Qualifications or separate. The instruction: prefer a few substantive sections over many thin ones. So we could have About the role, Responsibilities, Requirements, Qualifications (covering preferred education and experience). Skills maybe not needed if covered. We must not repeat job title. No Job ID. We must preserve all substantive information. So we need to include all details from intro, responsibilities, required expertise, preferred education, preferred experience. We must format lists with

• . For prose use

  . We must remove boilerplate: cookie/navigation/legal/EEO etc. There's none. We must not include sections like Benefits, Pay, Schedule because not present. Thus output:

  About the role

  Introduction text...

  Responsibilities

  • ... each responsibility

  Requirements

  Required Technical And Professional Expertise: ...

  maybe break into bullet list.

  Qualifications

  Preferred Education: Master's Degree

  Preferred Technical And Professional Experience: ... list

  We need to ensure we preserve all details. Let's extract intro text: "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." We need to keep that as prose. Now responsibilities list: - 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. Also note: "This job can be performed from anywhere in the US." That's a detail about location; could be included in About the role or maybe a separate note. Since we have no Schedule section, we could include it in About the role as a sentence. Now 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." We can turn each sentence into bullet list items. Preferred Education: "Master's Degree" 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. We need to preserve all. Now produce HTML. We must not include markdown fences. Just raw HTML. Let's craft. We'll wrap sections in

  and then content. For About the role: include intro paragraph plus location sentence. Thus:

  About the role

  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. This job can be performed from anywhere in the US.

  Now Responsibilities:

  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.
  Now Requirements:

  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.
  Now Qualifications:

  Qualifications