Senior Cloud Data & AI Architect — GenAI for Healthcare

Ontologize, Inc.

Mollet del Vallès

On-site

EUR 90,000 - 130,000

Full time

4 days ago

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Job summary

Ontologize, Inc. is seeking a Senior Cloud Data & AI Architect to design cloud-native architectures enabling scalable data, analytics, AI/GenAI, and agentic AI solutions across the healthcare portfolio.

You will partner with Data & AI, Security, Platform, and Healthcare teams to define standards and reference architectures using AWS, Snowflake, and Palantir Foundry. The role combines cloud engineering with data, AI/ML, and agentic workflow architecture to accelerate value delivery while ensuring

Qualifications

Bachelor's degree in computer science, information technology, engineering, or a related field.
6+ years of experience in cloud architecture, cloud engineering, or a closely related technical role.
Hands-on expertise designing solutions on AWS, including identity and access management, storage, compute, serverless, data, and security services.
Experience with at least one enterprise data platform, such as Snowflake or Palantir Foundry.
Experience designing and delivering AI/ML or GenAI infrastructure, including data pipelines, deployment, monitoring, and governance.
Knowledge of data governance, data quality, privacy, security, and regulated-data considerations, including PII/PHI and GxP-relevant contexts.
Proficiency in Python and SQL, plus infrastructure-as-code tooling such as Terraform or CloudFormation.
Experience designing or delivering GenAI and agentic AI solutions, including large language model application architecture, retrieval-augmented generation, agent orchestration, tool integration, evaluation, monitoring, and responsible‑AI controls.

Responsibilities

Design end-to-end cloud architectures on AWS and connected data platforms, including Snowflake and Palantir Foundry.
Define and govern reusable patterns for data ingestion, storage, processing, analytics, and AI/ML workflows.
Lead the architecture of AI/ML and GenAI capabilities, including MLOps, model monitoring, and risk controls.
Embed security, privacy, data governance, resilience, and cost management into solution designs from the start.
Evaluate platform and integration choices to improve performance, scalability, and total cost of ownership.
Promote architecture standards and best practices; create reference architectures, design documents, roadmaps, and runbooks; and mentor engineers and architects on automation and infrastructure as code.
Collaborate with cross-functional Healthcare teams to translate business priorities into scalable, secure, and compliant data and AI solutions.
Define reusable architecture patterns for agentic AI solutions, including agent orchestration, retrieval-augmented generation, tool integration, human-in-the-loop controls, evaluation, observability, and governance.