Senior Managing Consultant - AI Architect

IBM Consulting careers thrive on long-term client relationships and global collaboration. You’ll partner with leading companies across industries to shape their hybrid cloud and AI journeys, leveraging IBM technology, Red Hat, and strategic partnerships. The culture values curiosity, challenges norms, and fosters innovative solutions that drive real client impact. Growth and empathy are central, supporting your long-term career development while recognizing your unique skills and experiences.

Responsibilities

• Serve as a leader in defining solutions for clients, advocating for their needs while guiding the technical team through implementation.
• Collaborate with client stakeholders and internal partners to understand business problems, requirements, system constraints, and stakeholder concerns.
• Design centralized or distributed systems that address user requirements while ensuring optimal performance and efficiency.
• Lead end-to-end data architecture, including platform selection, technical architecture design, and application development.
• Interpret data, analyze results using statistical techniques, and provide ongoing reports to uncover key insights.

Leaders are expected to spend a minimum of three days a week in the workplace, subject to business needs. This role can be performed from anywhere in the U.S.

Requirements

• 10-12+ years of total experience in software engineering, data engineering, machine learning, or cloud architecture.
• Hands-on experience building and deploying ML models (supervised, unsupervised, deep learning).
• Expertise in model lifecycle and MLOps tools: MLflow, Kubeflow, Vertex AI, SageMaker.
• Proficiency in feature engineering and dataset management.
• Experience with Large Language Models (LLMs) and Generative AI, including:
  • LLM fine-tuning and RAG pipelines.
  • Vector databases.
  • Familiarity with OpenAI, Anthropic, Llama, and Hugging Face.
  • Prompt engineering, model evaluation, guardrails, and safety.
• Deep experience in at least one cloud platform.
• Architecture and system design experience, including high-level solution architecture diagrams.

Skills

• Data Engineering & Data Architecture:
  • Data pipelines: Spark, Airflow, Kafka.
  • Data lakes & warehouses: Snowflake, BigQuery, Redshift.
  • ETL/ELT design.
  • Data governance and quality frameworks.
• Security, Governance, and Responsible AI:
  • AI governance frameworks.
  • Privacy-by-design principles.
  • Model risk management.