AI Architect

Qode•Florida, FL

16h•Hybrid

About The Position

We are seeking an experienced AI Architect to design and govern end-to-end AI and ML architectures across a variety of enterprise use cases (e.g., prediction, personalization, recommendation, anomaly detection, automation). The ideal candidate will operate as a strategic technical leader, defining AI solution blueprints on Azure and AWS and ensuring scalable, secure, and compliant AI systems across products and teams. This role bridges business and technology by translating organizational objectives into robust AI architectures, reference patterns, and platform capabilities that can be reused across multiple domains and product lines. This is an exciting opportunity to shape the future of AI within our organization and contribute to cutting-edge projects.

Requirements

Proven experience designing and leading AI/ML solution architectures in production across multiple projects or products.
Ability to translate business use cases into architectural blueprints, roadmaps, and reusable platform components.
Strong understanding of ML techniques (supervised, unsupervised, basic deep learning) and their productionisation for real-world use cases.
Hands-on experience defining MLOps strategies using MLflow, Kubeflow, Airflow, Docker, and Kubernetes for large-scale deployments.
Expert knowledge of Azure ML and AWS SageMaker, plus core services (S3, EC2, Lambda, Azure Functions, identity, networking, monitoring).
Experience designing multi-environment (dev/test/prod) AI platforms with robust CI/CD, model promotion, and rollback strategies.
Understanding of data privacy, security, and responsible AI principles (fairness, transparency, explainability, auditability).
Experience defining policies and controls for model governance and risk management.
Strong Python skills and familiarity with common ML libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
Solid grounding in CI/CD (GitHub Actions, Azure DevOps, AWS CodePipeline) and monitoring/logging solutions (Prometheus, Grafana, or cloud-native equivalents).
7–10 years overall experience (minimum 5+ years in AI/ML, with strong MLOps and cloud experience).

Nice To Haves

Preferred Qualifications include prior experience as an AI Architect, ML Architect, or AI Solutions Architect in an enterprise environment.
Certifications such as Azure Solutions Architect Expert, Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, or equivalent architecture credentials are also highly desirable.
Exposure to generative AI and LLM architectures, including RAG, orchestration frameworks, and fine-tuning strategies is a plus.
A demonstrated track record of leading cross-functional AI initiatives from concept through architecture, implementation, and scaling is also preferred.

Responsibilities

Define and own end-to-end AI architecture for multiple enterprise use cases, including data flows, model lifecycle, and serving patterns across Azure and AWS.
Design AI/ML solution blueprints covering data ingestion, feature stores, training pipelines, model registry, deployment, monitoring, retraining, and decommissioning.
Establish and standardize MLOps frameworks (MLflow/Kubeflow/Airflow, Docker, Kubernetes) and reference implementations that AI/ML engineers and data scientists can reuse across teams.
Collaborate with product, data, and business stakeholders to identify AI opportunities, shape solution options, and align AI architectures with business and non-functional requirements (scalability, reliability, cost, latency).
Define and enforce governance for model approval, explainability, versioning, drift monitoring, and compliance with data privacy and regulatory requirements relevant to the organization.
Guide the selection and integration of cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Azure Functions, API gateways, monitoring stacks) into cohesive AI platforms.
Work closely with security, compliance, data, and enterprise architecture teams to ensure AI solutions meet standards for security, resilience, observability, and cost optimisation.
Provide technical leadership and mentoring to AI engineers, ML engineers, and data scientists on best practices in AI architecture, MLOps, and cloud-native design.
Create and maintain architectural artefacts: high-level designs, detailed solution diagrams, standards, and documentation for AI platforms and reference solutions.