AI and Data Engineer

About the company

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

Full job description

We are building a team of engineers in Edmonton, AB, Canada to develop a first-of-its-kind synthetic persona platform — an AI system that models human behavior, preferences, and decision-making at scale. This role sits at the intersection of software engineering, data engineering and applied AI: you will design the pipelines that power the personas and , the models and the platform deployments that bring them to life.

This is not a maintenance role. The platform is being built from the ground up, and every technical decision you make will shape what it becomes. You will work alongside researchers, data scientists, and product engineers in a small, high-trust team where curiosity and ownership are the norm.

What You Will Do

• Design and build a scalable platform and the data pipelines that ingest, transform, and serve structured and unstructured data to AI models
• Develop and iterate on AI/ML components — including LLM-based agents, embedding models, and behavioral simulation layers — that power synthetic persona generation
• Architect and maintain the data infrastructure underpinning persona modeling: feature stores, vector databases, data lakes, and real-time serving layers
• Collaborate with researchers to translate persona logic and behavioral frameworks into working system components
• Write production-quality code, participate in code reviews, and contribute to engineering standards for the team
• Monitor model and pipeline performance in production; identify and resolve issues proactively
• Contribute to system design discussions and help shape the technical roadmap

Qualifications and requirements

Must be able to commute to our Edmonton, AB, Canada office

Core Technical Skills

• 3–5 years of hands-on experience in software engineering, data engineering, ML engineering, or a closely related role
• Proficiency in Python and at least one data processing framework (Spark, dbt, Airflow, Prefect, or similar)
• Experience building and deploying ML models or AI components in a production environment
• Familiarity with LLMs and modern AI tooling: prompt engineering, fine-tuning, RAG pipelines, orand agent frameworks (LangChain, LangGraph LlamaIndex, CrewAI, or equivalent)
• Solid understanding of data modeling, schema design, and the tradeoffs between different storage paradigms (relational, document, vector, columnar)
• Experience with cloud data infrastructure — AWS, GCP, or Azure — and comfort operating in a cloud-native environment

Systems & Engineering Mindset

• Ability to reason about system architecture: latency, throughput, scalability, and data consistency tradeoffs
• Experience with APIs, microservices, or event-driven architectures (Kafka, Pub/Sub, or similar)
• Comfort working across the stack — from raw data ingestion through to model serving and API exposure
• Strong debugging instincts and a habit of writing observable, testable code

How You Work

• Intrinsically motivated — you pursue hard problems because they interest you, not because someone handed you a ticket
• Comfortable with ambiguity; you can move forward when the requirements are still forming
• Collaborative by default — you ask questions, share context early, and bring others along
• You read papers, experiment on weekends, and have opinions about how AI systems should be built

Nice to Have

• Experience with Snowflake
• Experience with behavioral modeling, simulation, or agent-based systems
• Background in NLP, computational social science, or user modeling
• Contributions to open-source AI or data tooling
• Familiarity with synthetic data generation techniques or privacy-preserving ML
• Experience working in a startup or early-stage product environment

Additional information

Synthetic personas are one of the most technically interesting and practically consequential challenges in applied AI right now. The platform you help build will be used to simulate human decision-making in ways that have real product and business impact. You will have direct influence over architectural decisions, meaningful ownership of your components, and a front-row seat to a research area that is evolving fast.

*Must be able to work on-site at our Edmonton, AB, Canada location

Must be able to commute to our Edmonton, AB, Canada office

Core Technical Skills

• 3–5 years of hands-on experience in software engineering, data engineering, ML engineering, or a closely related role
• Proficiency in Python and at least one data processing framework (Spark, dbt, Airflow, Prefect, or similar)
• Experience building and deploying ML models or AI components in a production environment
• Familiarity with LLMs and modern AI tooling: prompt engineering, fine-tuning, RAG pipelines, orand agent frameworks (LangChain, LangGraph LlamaIndex, CrewAI, or equivalent)
• Solid understanding of data modeling, schema design, and the tradeoffs between different storage paradigms (relational, document, vector, columnar)
• Experience with cloud data infrastructure — AWS, GCP, or Azure — and comfort operating in a cloud-native environment

Systems & Engineering Mindset

• Ability to reason about system architecture: latency, throughput, scalability, and data consistency tradeoffs
• Experience with APIs, microservices, or event-driven architectures (Kafka, Pub/Sub, or similar)
• Comfort working across the stack — from raw data ingestion through to model serving and API exposure
• Strong debugging instincts and a habit of writing observable, testable code

How You Work

• Intrinsically motivated — you pursue hard problems because they interest you, not because someone handed you a ticket
• Comfortable with ambiguity; you can move forward when the requirements are still forming
• Collaborative by default — you ask questions, share context early, and bring others along
• You read papers, experiment on weekends, and have opinions about how AI systems should be built

Nice to Have

• Experience with Snowflake
• Experience with behavioral modeling, simulation, or agent-based systems
• Background in NLP, computational social science, or user modeling
• Contributions to open-source AI or data tooling
• Familiarity with synthetic data generation techniques or privacy-preserving ML
• Experience working in a startup or early-stage product environment

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