Remote Data & AI Engineer Consultant

IBM Computing

Buffalo (NY)

On-site

USD 110,000 - 150,000

Full time

4 days ago

Be an early applicant

Application generator

Stand out for this role — generate a tailored resume and cover letter in about a minute.

Get past ATS filters

Job summary

IBM Consulting seeks a Consultant, AI and Data Engineer to join a growing team. You will build Snowflake data foundations and AI solutions, spanning data pipelines, governance, and model deployment for client projects.

The ideal candidate blends data engineering with applied AI, working with Snowflake Cortex, LangChain, and vector tools, while collaborating with architects, analysts, and data scientists across fast-paced engagements.

Qualifications

Spanning data engineering and applied AI; consulting or professional services experience is desirable.
Strong SQL and Python; experience with dbt, Spark, or Snowpark for transformation.
Experience building ETL / ELT ingestion pipelines and developing data warehouses on cloud platforms.
Hands-on experience building LLM-based applications: prompt engineering, retrieval-augmented generation, vector stores and embeddings.
Working knowledge of ML fundamentals and trade-offs between LLM, classical ML, and rules-based approaches.
Proficiency with cloud platform services for data and AI workloads (AWS, Azure, or GCP).
Experience with Git-based version control, automated testing, CI/CD, and code review.
Understanding of data governance, security, and compliance best practices.
Strong communication skills to explain data and AI concepts to non-technical stakeholders.
Ability to work in cross-functional, Agile teams in a dynamic environment.

Responsibilities

Data Pipeline Development: Design, build, and operate ETL / ELT pipelines and data models on Snowflake.
AI Solution Development: Build, evaluate, and deploy LLM-based applications and ML models to solve client problems.
AI-Ready Data Engineering: Prepare, chunk, embed, and govern data for AI consumption with vector search and semantic layers.
Snowflake AI Platform: Implement solutions using Snowflake Cortex and related services; integrate with external model providers.
Evaluation and Reliability: Define evaluation datasets and metrics; implement testing and monitoring; iterate prompts and models.
Implementation and Deployment: Deploy solutions to cloud environments with CI/CD and IaC practices.
Client Engagement: Understand requirements and present options; integrate data and AI into client processes.
Governance and Security: Apply data governance and privacy practices across data pipelines and AI systems.
Documentation and Knowledge Transfer: Produce documentation for pipelines, models, prompts, and architectures.
Continuous Learning: Track Snowflake and AI ecosystem developments and provide practical recommendations.