Bcg Platinion | Lead It Architect - AI Platforms

BCG BCG · Consulting · Atlanta, GA +18 · Technology and Engineering

Lead IT Architect role focused on designing and guiding the implementation of agentic AI platforms, integrating LLMs, data pipelines, and enterprise systems. The role involves system architecture design, evaluating AI frameworks, defining integration patterns like RAG, and supporting the transition from prototype to production. It requires a blend of architectural thinking and hands-on technical experience.

What you'd actually do

  1. Design system architectures for AI and LLM-based solutions, balancing scalability, performance, modularity, and operational complexity.
  2. Evaluate emerging Ai frameworks and tooling (e.g., LangChain, LlamaIndez, LangGraph, Strands, Google ADK, Semantic Kernel, etc.) and recommend fit-for-purpose usage.
  3. Design agentic AI solutions to enable intelligent workflow automation, including task decomposition, memory usage, and orchestration patterns.
  4. Define AI integration patterns such as RAG for context management, model orchestration, prompt workflows, and enterprise system connectivity.
  5. Contribute to architectural standards and design principles for model lifecycle management, data lineage, and responsible AI practices.

Skills

Required

  • AI platform architecture
  • Agentic system design
  • LLM integration
  • Data pipeline integration
  • Enterprise system integration
  • Scalability
  • Performance optimization
  • Modularity
  • Operational complexity management
  • Evaluation of AI frameworks and tooling
  • Workflow automation
  • Task decomposition
  • Orchestration patterns
  • RAG implementation
  • Prompt engineering
  • Model lifecycle management
  • Data lineage
  • Responsible AI practices
  • Hybrid pipeline architecture (batch training, real-time inference)
  • Cost optimization
  • Latency optimization
  • Operational risk management
  • Prototyping
  • Architectural validation
  • System performance
  • Observability
  • Governance
  • Development standards (versioning, testing, deployment, monitoring)

Nice to have

  • Hands-on technical leadership
  • Agile methodologies
  • Client stakeholder management
  • Internal leader collaboration

What the JD emphasized

  • design and guide the implementation of intelligent, agentic AI platforms
  • design end-to-end AI platform solutions
  • define and deliver scalable AI architectures that integrate large language models (LLMs), data pipelines, and enterprise systems
  • Design agentic AI solutions to enable intelligent workflow automation
  • Define AI integration patterns such as RAG for context management, model orchestration, prompt workflows, and enterprise system connectivity

Other signals

  • design and guide the implementation of intelligent, agentic AI platforms
  • design end-to-end AI platform solutions
  • define and deliver scalable AI architectures that integrate large language models (LLMs), data pipelines, and enterprise systems
  • Design agentic AI solutions to enable intelligent workflow automation
  • Define AI integration patterns such as RAG for context management, model orchestration, prompt workflows, and enterprise system connectivity