Design and launch a scalable enterprise artificial intelligence platform in a major public cloud environment supporting both generative and predictive models.
Establish standards for prompt orchestration, knowledge-grounded response pipelines, and full lifecycle management of machine learning systems.
Build a multi-layered data architecture that connects operational and transactional systems into a unified intelligence environment.
Align AI initiatives with enterprise cloud, security, and governance strategies in partnership with architecture leadership.
Research and adopt emerging AI platform capabilities and determine business value and scalability.
Model Development, Deployment & Operations
Lead development and tuning of large language models, deep learning models, and domain-adapted AI solutions using managed services and custom frameworks.
Own end-to-end model lifecycle: training → validation → deployment → monitoring → retraining.
Implement knowledge-grounded AI workflows using vector search and semantic retrieval technologies.
Ensure highly available model hosting, version control, and scalable inference orchestration.
Intelligent Data Processing & Automation
Architect ingestion pipelines that process structured and unstructured content including documents, images, voice, and communications.
Build AI-driven automation for classification, summarization, and information extraction across large document sets.
Optimize performance and cost using event-driven and serverless patterns.
MLOps, DevOps & Infrastructure Automation
Implement continuous integration and delivery for machine learning systems.
Standardize environment provisioning across development, test, and production environments using infrastructure-as-code.
Integrate monitoring, observability, and auditability across all AI services.
Enable containerized model deployment strategies.
Responsible AI, Governance & Security
Establish responsible AI practices including explainability, bias detection, and safe response generation.
Implement safeguards to reduce incorrect or unsafe AI outputs.
Ensure adherence to data protection regulations and enterprise security requirements.
Partner with security, legal, and compliance teams to operationalize governance controls.
Leadership & Collaboration
Lead and mentor engineers and data scientists across multiple teams.
Partner with business leaders to identify and prioritize high-impact AI use cases.
Build reusable frameworks, internal standards, and knowledge-sharing programs.
Promote experimentation and responsible adoption of AI technologies across the organization.
What We’re Looking For
Degree in Computer Science, Artificial Intelligence, Data Engineering, or related discipline (advanced degree preferred)
~12+ years in software or data engineering with ~8+ years building and deploying AI/ML systems
Strong hands-on experience building AI platforms in a cloud environment
Deep experience with Python and modern ML frameworks
Experience designing knowledge-grounded generative AI solutions using vector retrieval
Expertise in ML lifecycle automation, deployment pipelines, and monitoring
Experience operating in regulated or high-compliance environments
Demonstrated leadership mentoring technical teams
Ability to communicate complex technical topics to non-technical stakeholders