Senior Managing Consultant - AI Architect

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.

This role can be performed from anywhere in the US. Leaders are expected to spend time with their teams and clients and are generally expected to be in the workplace a minimum of three days a week, subject to business needs.

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 solutions (architectures) for the client.
• Design centralized or distributed systems that address user requirements and perform efficiently and effectively.
• 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.

Requirements

Required Technical and Professional Expertise

• 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).
• Model lifecycle and MLOps: MLflow, Kubeflow, Vertex AI, SageMaker.
• Feature engineering and dataset management.
• Large Language Models (LLM) and Generative AI experience, including:
  • LLM fine-tuning, RAG pipelines, vector databases.
  • Familiarity with OpenAI, Anthropic, Llama, 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.

Preferred Technical and Professional Experience

• Experience in data engineering and data architecture, including:
  • Data pipelines: Spark, Airflow, Kafka.
  • Data lakes and warehouses: Snowflake, BigQuery, Redshift.
  • ETL/ELT design.
  • Data governance and quality frameworks.
• Security, governance, and responsible AI experience, including:
  • AI governance frameworks.
  • Privacy-by-design.
  • Model risk management.