AI Architect

Job Title:

Experience:

Location : Remote

Role Overview

AI Architect

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.

Key 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.

Required Skills

Architecture & Leadership

• 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.

AI / ML & MLOps

• 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.

Cloud Platforms (Azure & AWS)

• 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.

Governance, Security & Compliance

• Understanding of data privacy, security, and responsible AI principles (fairness, transparency, explain ability, auditability).
• Experience defining policies and controls for model governance and risk management.

Tooling & Engineering Foundations

• 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).

Preferred Qualifications

• 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.
• Exposure to generative AI and LLM architectures, including RAG, orchestration frameworks, and fine‑tuning strategies.
• Demonstrated track record of leading cross‑functional AI initiatives from concept through architecture, implementation, and scaling.