AI Engineer

• Own the reference architectures for machine learning and Generative AI; define build-versus-buy and publish a 12–18-month AI platform roadmap aligned to business priorities.
• Establish core services—data pipelines, feature store/model registry, prompt/model CI/CD, evaluation harnesses, observability (quality, safety, latency, cost), and automated rollback.
• Design end-to-end solutions combining predictive models, large language models, and integrations with enterprise applications and data sources; ensure patterns for grounding, retrieval, and guardrails.
• Stand up model risk and ethics guardrails; define evaluation metrics (factuality, hallucination rate, toxicity, bias), documentation, approvals, and live monitoring for drift.
• Architect for AWS, Azure, and/or GCP and/or on-premise with secure networking, secrets management, and cost controls.
• Partner with enterprise/platform teams to integrate AI into workflows, APIs, and UIs; ensure identity, authorization, auditability, and reliability SLAs.
• Provide architectural governance, conduct design reviews, train users, and lead vendor/tool evaluations.
• Track and selectively adopt advances in LLMs, agents/orchestrations, vector databases, and evaluation methods; run lightweight proofs to de-risk delivery.

AI Architect

AI Developer – Machine Learning, Vision Engineer

ERP Analytics & AI Architect Lead

AI Architect

AI Engineer