AI Application Architect

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

As an Architect in IBM Consulting you'll serve as a leader in defining solutions for clients. You'll be the advocate for the client while guiding the technical team to implementation.

You'll collaborate with client stakeholders and internal partners to understand the business problem and requirements constraints of the system and concerns of the various stakeholders to systematically transform detailed solutions (architectures) for the client.

Your primary responsibilities include:

• Innovative Systems Design for Optimal Performance: Design centralized or distributed systems that both address the user's requirements and perform efficiently and effectively.
• End-to-End Data Architecture Leadership: Manage end-to-end data architecture starting from selecting the platform designing a technical architecture and developing the application.
• Data Analysis and Insightful Reporting: Interpret data analyze results using statistical techniques and provide ongoing reports discovering key insights.

Bachelor's Degree

• 7–12+ years total experience in software engineering data engineering machine learning or cloud architecture.
• Deep experience in at least one cloud platform.
• Architecture & System Design Experience; high-level solution architecture diagrams.

Hands-on experience is expected in:

• Building and deploying ML models (supervised unsupervised deep learning).
• Model lifecycle & MLOps: MLflow Kubeflow Vertex AI SageMaker.
• Feature engineering and dataset management.

Large Language Models & Generative AI Experience

• Experience with LLM fine-tuning RAG pipelines vector databases.
• Familiarity with OpenAI Anthropic Llama Hugging Face.
• Prompt engineering model evaluation guardrails & safety.

Experience in Data Engineering & Data Architecture

• Data pipelines: Spark Airflow Kafka.
• Data lakes & warehouses: Snowflake BigQuery Redshift.
• ETL/ELT design.
• Data governance & quality frameworks.

Security Governance and Responsible AI Experience

• AI governance frameworks.
• Privacy-by-design.
• Model risk management.