AI and Data Engineer

About the role

Blend is hiring an AI and Data Engineer in Edmonton, Alberta, to help build a synthetic persona platform: an AI system that models human behavior, preferences and decision-making at scale. The role sits at the intersection of software engineering, data engineering and applied AI, designing the pipelines that feed the personas and the models and deployments that bring them to life. It is not a maintenance role: the platform is being built from the ground up, and you will work alongside researchers, data scientists and product engineers in a small, high-trust team.

Key responsibilities

• Design and build a scalable platform and data pipelines that ingest, transform and serve structured and unstructured data to AI models.
• Develop and iterate on AI/ML components such as LLM-based agents, embedding models and behavioral simulation layers that power synthetic persona generation.
• Architect and maintain the data infrastructure: feature stores, vector databases, data lakes and real-time serving layers.
• Work with researchers to turn persona logic and behavioral frameworks into working system components.
• Write production-quality code, take part in code reviews and help set engineering standards.
• Monitor model and pipeline performance in production and resolve issues proactively.
• Contribute to system design discussions and help shape the technical roadmap.

Required profile

• 3-5 years of hands-on experience in software engineering, data engineering, ML engineering or a closely related role.
• Ability to commute to and work on-site in Edmonton, Alberta.
• Intrinsic motivation, comfort with ambiguity and collaborative approach.

Required skills

• Python and at least one data processing framework (Spark, dbt, Airflow, Prefect or similar).
• Building and deploying ML models or AI components in production.
• Familiarity with LLMs and modern AI tooling: prompt engineering, fine-tuning, RAG pipelines and agent frameworks (LangChain, LangGraph, LlamaIndex, CrewAI or equivalent).
• Data modeling, schema design and storage trade-offs (relational, document, vector, columnar).
• Cloud data infrastructure (AWS, GCP or Azure) and cloud-native environments.
• APIs, microservices or event-driven architectures (Kafka, Pub/Sub or similar); debugging and testable code.
• Nice to have: Snowflake, behavioral modeling or agent-based systems, NLP or user modeling background, open-source contributions, synthetic data or privacy-preserving ML, startup experience.