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 deliver measurable client impact while supporting your long-term career growth.

Responsibilities

• Serve as a technical leader, defining and advocating for client solutions while guiding implementation teams.
• Collaborate with client stakeholders and internal partners to understand business problems, requirements, system constraints, and stakeholder concerns.
• Design centralized or distributed systems that meet user requirements and deliver optimal performance.
• Lead end-to-end data architecture, including platform selection, technical 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 per 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.
• Proven track record in architecture and system design, including high-level solution architecture diagrams.

Preferred Qualifications

• Experience in data engineering and data architecture.
• Proficiency with data pipelines: Spark, Airflow, Kafka.
• Experience with data lakes and warehouses: Snowflake, BigQuery, Redshift.
• ETL/ELT design expertise.
• Knowledge of data governance and quality frameworks.
• Experience with security, governance, and responsible AI, including:
  • AI governance frameworks.
  • Privacy-by-design principles.
  • Model risk management.