We use PostHog and Clarity to improve the site, and Meta Pixel to measure page views and ad effectiveness. Usage data may include page addresses and browser identifiers. You can opt out of collection at any time. Details in the Privacy Policy
More fit checks, tailored CVs, and complete prep for every role.
Job Description
About the role
Vi is an enterprise AI platform for health enterprises - healthcare, biopharma, and wellness. We deploy agentic AI and predictive models into production environments where the output drives next best actions for patients, care teams, and operations to deliver ROI and improve health outcomes.
Data Web is the data foundation under the platform - the third-party health and consumer data we license, resolve, and turn into assets our customers and our models run on. We are looking for an AI Engineer to own that path end-to-end, from ingestion and identity resolution through features, retrieval, and production delivery, and to own the read on what comes out of it.
Reporting directly to the VP of R&D, the AI Engineer is a hands-on IC role at the crossroads of data science and data engineering. You build the data, you build the AI that runs on it, and you judge the result.
What you'll do
Build and own data pipelines: Monitored ingestion for high-volume third-party health and consumer feeds.
Put AI to work on the data itself: LLMs and ML for parsing, normalizing, classifying and matching.
Build the feature and retrieval layer: Versioned features, embeddings and retrieval our models and agents run on.
Evaluate: Implement evaluation frameworks, benchmarks, and metrics to track performance.
Build across the stack: Builder mindset - comfortable with Backend services and internal tooling.
Do the first read on what you build: Is there signal, and what is this asset good for and turn data assets into products.
Partner cross-functionally: Turn product, client and compliance requirements into production systems.
Requirements
2–3 years of overall software experience, including background in Data Science, ML modeling, and shipping production-grade data systems.
Production AI & Data Science: experience building ML/DS models, evaluating data signals, and deploying the underlying data systems required to run them in production.
Production LLM & AI Engineering: Hands-on experience shipping LLM capabilities combined with an AI-first development workflow.
ETL/ELT pipelines and Lakehouse architectures using Python, SQL,Spark on AWS (Glue, EMR, Athena, Iceberg).
Software & DevOps: Strong backend engineering fundamentals, CI/CD, Docker, workflow orchestration, and Infrastructure as Code (AWS CDK/CloudFormation).
High Ownership & Velocity: High agency, a strong sense of urgency, and full lifecycle ownership of production systems from architecture to monitoring.