Solutions Architect, AI Systems
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Role details
Tech stack
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Job description
- Partner with executive, strategy, and client teams to scope AI and data solutions from an ambiguous business problem to a concrete technical plan.
- Design the system, then build and ship the first working version yourself - architecture and delivery sit with the same person.
- Build and deploy functional tools, models, and internal products on our proprietary data infrastructure (Snowflake, BigQuery) - not dashboards alone.
- Write and maintain working code (Python, SQL, APIs) to operationalize models and pipelines - doesn’t need to be enterprise-grade, needs to run.
- Deploy applied AI methods - LLMs, embeddings, predictive modeling - as real, working features, not proofs of concept that stall at the deck stage.
- Collaborate with Data Engineering and AI teams to keep infrastructure aligned and data quality high as you build.
- Build internal enablement tools that let non-technical teams use and scale what you’ve shipped.
- Own your solutions past launch - monitor real usage, iterate, and improve.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field - or equivalent experience demonstrating technical excellence and business fluency.
- 5+ years in Solutions Architecture, Applied AI/ML Engineering, or Data Engineering, with a track record of shipped, working software - not just designs, specs, or dashboards.
- Proficient in Python (or a comparable language) and SQL; comfortable writing and deploying code that runs in production, even if it isn’t polished.
- Experience building and deploying models, pipelines, or applications on cloud data platforms such as Snowflake or BigQuery.
- Working knowledge of LLMs, embeddings, and applied AI methods, with real experience shipping them into working tools.
- BI tool fluency (Sigma, Tableau, Looker, Power BI) is a plus - not a substitute for engineering ability.
- Product mindset - comfortable owning a problem from an ambiguous ask through to a deployed, working solution.
- Comfortable operating in ambiguous, fast-moving problem spaces without a large team around you.
- Background in marketing, the creator economy, or social intelligence is a strong asset.
- Please include a link to something you’ve built and shipped - a repo, a deployed app, or a demo - with your application.
About the company
Viral Nation,
we specialize in building social-first ecosystems for brands to connect with the modern consumer journey. Our integrated solutions align strategy, talent, media, and technology with culturally relevant creativity to scale the world’s fastest-growing digital brands. Viral Nation offers a fluid, creative, and growth-oriented environment that will support your ambitions to apply your talents in an open, collaborative, and fast-paced culture. Our ability to stay at the forefront of the industry has fuelled our success and will guide us in paving the path forward. We’re driven to push boundaries and think beyond today to deliver strategies, and we’re just getting started.
While we continuously exceed our goals, we need your help â?? our success is only as great as our people. Strong performance leads to high expectations, and we must keep raising the bar!
We have one of the largest and most advanced proprietary data infrastructures in the world, spanning social, customer, and sourced datasets. The next step is turning that data into scalable, AI-driven products - not just models and dashboards, but working software that ships., At Viral Nation, compensation is structured, performance-led, and market-aligned. Pay decisions are intentional, based on role requirements, performance, and market alignment by country, and are set to maintain internal pay equity across comparable roles and levels.
Compensation progression is tied to demonstrated performance, expanded scope, and sustained contribution over time, not tenure alone. Employee compensation is reviewed regularly through formal performance reviews and check-ins to ensure alignment with role expectations and impact.
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