AI Engineer

What You’ll Do AI Platform Architecture & Strategy

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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