AI Architect

Introduction

A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.

You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio, including Software and Red Hat.

Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role as an AI Architect, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in groundbreaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth.

Your Role And Responsibilities

As an AI Architect at IBM, you will lead the design and implementation of cutting-edge AI/ML solutions, ensuring they are scalable, secure, and compliant. Your responsibilities span the entire AI/ML lifecycle, from architectural design to performance optimization and cross-team collaboration.

Architecture & Model Lifecycle Design

• Define AI/ML architecture patterns (training pipelines, inference pipelines, deployment).
• Design scalable MLOps workflows, including CI/CD, model registries, and environments.
• Choose appropriate algorithms, feature stores, and deployment patterns.

Governance, Security & Compliance

• Ensure model governance (versioning, approvals, drift detection, lineage).
• Apply security controls (RBAC, secrets, isolation of compute).
• Maintain AI compliance standards (interpretability, auditability, reproducibility).

Performance & Reliability

• Evaluate performance of training and inference pipelines (latency, cost, throughput).
• Lead root-cause analysis for model failures and pipeline bottlenecks.
• Improve monitoring and observability (drift metrics, accuracy tracking, ML telemetry).

Cross‑Team Collaboration

• Collaborate with Data Scientists to refine feature engineering and modeling approaches.
• Align Data Engineering teams on data availability and feature store structures.
• Coordinate with Cloud Architects on infrastructure scaling and optimization.

Strategic & Technical Leadership

• Provide architectural guidance to Data Scientists and MLOps Engineers.
• Establish AI technical standards, patterns, and reusable components.
• Review ML code, pipeline definitions, and architecture proposals.

Technical Requirements

To succeed in this role, you should possess the following technical and professional expertise:

• AI/ML solution architecture experience.
• Hands-on experience with Azure ML, Databricks, Python, and Apache Spark.
• Strong background in feature engineering and model lifecycle design.
• Experience with MLOps, including CI/CD for ML, pipelines, and model registries.
• Design and implementation of Data Lake / Delta Lake architectures.
• API design for model deployment and consumption.
• Solid understanding of ML governance, drift detection, and model monitoring.
• Knowledge of cloud security and identity principles.

Behavioral Skills

• Strategic thinking mindset.
• Strong communication skills with business stakeholders.
• Ability to simplify complex technical concepts.
• Technical leadership and mentoring capabilities.

Education

• Bachelor’s degree completed (Computer Science, Engineering, or related field).

Language

• Advanced English.

Preferred Technical And Professional Experience

• Nice to have: experience in utilities projects.

Key skills/competency

• AI/ML Architecture
• MLOps
• Azure ML
• Databricks
• Python
• Apache Spark
• Data Lake/Delta Lake
• Model Governance
• Cloud Security
• Feature Engineering