Senior Data and AI Engineer, Material Bank

• This is an exciting opportunity for someone who is, at their core, a passionate data engineer with deep curiosity about AI and significant experience building strong data foundations before expanding into applied AI and agent based systems
• We are looking for someone who enjoys solving complex technical problems, experimenting with emerging technologies, and turning ambiguous ideas into scalable, production ready solutions
• Deep experience and genuine passion for data engineering, with strong instincts around data modeling, pipeline architecture, scalability, data quality, and building reliable platforms. Strong data foundations are core to this role
• 5+ years of experience in data engineering, AI/ML engineering, or related fields, including recent hands on experience building and shipping LLM powered applications or AI agents into production environments
• Experience building production APIs and services, including MCP servers and REST based architectures
• Strong understanding of modern agent development patterns including RAG, vector search, prompt engineering, tool/function calling, and frameworks such as LangChain, LangGraph, or LlamaIndex
• Deep expertise in Snowflake, including performance optimization, warehouse architecture, and scalable data modeling approaches such as dimensional modeling or Data Vault
• Production experience with dbt and Airflow, including building and maintaining semantic or metrics layers
• Strong Python engineering skills and solid experience working within AWS environments including services such as S3, IAM, Lambda, ECS, or similar
• Hands on experience using AI powered engineering tools such as Claude Code or similar development accelerators as part of real world engineering workflows
• Excitement about specializing deeply in Snowflake Cortex and helping define our long term AI platform strategy

Desirable

• We’re looking for a Senior Data & AI Engineer to lead the design, development, and operation of AI agents that power intelligent experiences across the Material Bank platform
• This role sits at the intersection of data engineering, applied AI, and platform innovation, with the opportunity to shape how AI is embedded into the core of our business and customer experience
• You’ll be the technical lead defining how we build AI agents, with direct access to the teams interfacing with Snowflake with room to influence architecture decisions, and the chance to work across the full AI stack from data modeling and semantic layers to agent orchestration and production operations
• Working hands-on with Snowflake Cortex as our primary AI platform, you will help push the boundaries of what modern AI systems can do in an enterprise environment while helping define the future of intelligent experiences at Material Bank
• Design, build, and operate production grade AI agents, owning the full lifecycle from prototyping and evaluation through deployment, monitoring, and continuous improvement
• Lead the development of scalable AI and data services, including MCP servers and REST APIs that expose intelligent capabilities to products, applications, and internal teams
• Serve as our internal expert on Snowflake Cortex, going deep on Cortex Agents, Cortex Analyst, and Cortex Search while partnering directly with Snowflake’s account and product teams to influence capabilities and shape how we apply the platform
• Apply modern agent architecture patterns including RAG, tool use, orchestration, memory, and evaluation frameworks to build reliable, accurate, and cost efficient AI systems
• Partner closely with Analytics & Insights team to design and maintain semantic and metrics layers that create consistent business definitions across AI, analytics, and reporting use cases
• Build and maintain scalable data pipelines, transformations, and models that power AI workloads using Snowflake, dbt, and Airflow
• Collaborate across data, product, analytics, and engineering teams to translate ambiguous business problems into well designed AI and data solutions
• Establish engineering standards and best practices for agentic systems, including observability, evaluation, prompt management, governance, and operational guardrails