AI Architect - Managing Consultant

About the role

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success—you’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Responsibilities

• Serve as a leader in defining solutions for clients, advocating for their needs while guiding the technical team to implementation.
• Collaborate with client stakeholders and internal partners to understand business problems, requirements, system constraints, and stakeholder concerns to systematically transform detailed architectures for the client.
• Innovative Systems Design for Optimal Performance: Design centralized or distributed systems that address user requirements and perform efficiently.
• End-to-End Data Architecture Leadership: Manage data architecture, including platform selection, technical design, and application development.
• Data Analysis and Insightful Reporting: Interpret data, analyze results using statistical techniques, and provide ongoing reports to uncover key insights.
• Build and deploy ML models (supervised, unsupervised, deep learning).
• Implement model lifecycle and MLOps using tools like MLflow, Kubeflow, Vertex AI, or SageMaker.
• Perform feature engineering and dataset management.
• Work with Large Language Models (LLMs) and Generative AI, including fine-tuning, RAG pipelines, and vector databases.
• Apply prompt engineering, model evaluation, guardrails, and safety measures.

Requirements

• 7–12+ years of total experience in software engineering, data engineering, machine learning, or cloud architecture.
• Deep experience in at least one cloud platform.
• Architecture and system design experience, including high-level solution architecture diagrams.
• Hands-on experience with LLM fine-tuning, RAG pipelines, and vector databases.
• Familiarity with OpenAI, Anthropic, Llama, or Hugging Face.

Preferred Qualifications

• Data Engineering & Data Architecture: Experience with data pipelines (Spark, Airflow, Kafka), data lakes/warehouses (Snowflake, BigQuery, Redshift), ETL/ELT design, and data governance/quality frameworks.
• Security, Governance, and Responsible AI: Experience with AI governance frameworks, privacy-by-design, and model risk management.