AI Solutions Architect - London | Bcg Platinion

BCG BCG · Consulting · London, United Kingdom · Technology and Engineering

This role focuses on designing and implementing AI-driven architectures and platforms for enterprise clients, specifically focusing on agentic systems, autonomous delivery pipelines, and AI-powered software factories. The architect will translate business requirements into scalable technical solutions, embed AI into existing landscapes, and establish trusted AI engineering environments with governance and validation mechanisms.

What you'd actually do

  1. You design AI-driven target architectures and conceptualize AI-based software factories, from “dark software factories” to autonomous software development at enterprise scale
  2. You develop AI-powered delivery pipelines that translate development processes, engineering standards, and architectural guidelines into machine-executable workflows
  3. You establish trusted AI engineering environments by designing governance mechanisms with multi-layered validation, clear identity boundaries, and end-to-end traceability
  4. You implement evaluation and security mechanisms such as automated testing and evaluation pipelines, architecture compliance checks, and red-teaming approaches for AI-generated systems
  5. You translate enterprise knowledge into machine-readable structures such as architecture decisions, domain models, APIs, and business rules to enable autonomous development

Skills

Required

  • Degree in Computer Science, Information Systems, AI Engineering, or a related field
  • Strong understanding of legacy systems, enterprise solutions, and current market trends (e.g., agentic AI, LLM orchestration, AI-native architectures)
  • Hands-on experience with AI systems, such as LLM-based applications, AI agents, or machine learning platforms
  • Familiarity with modern software delivery approaches, including DevSecOps, CI/CD pipelines, automated testing, and platform engineering
  • Understanding of concepts like agent assembly lines, execution harnesses, or context engineering
  • Ability to translate complex business requirements into technical architectures
  • Effective communication with both technical and non-technical stakeholders
  • Experience in interdisciplinary teams and global environments

Nice to have

  • Azure AI Foundry (Understanding of various GenAI platform & middleware and how they fit in GenAI architecture e.g., AWS Bedrock, Google Vertex, Langchain or LlamaIndex)

What the JD emphasized

  • design agentic platforms
  • autonomous delivery pipelines
  • intelligent systems
  • AI-driven architectures
  • AI-based software factories
  • autonomous software development
  • AI-powered delivery pipelines
  • trusted AI engineering environments
  • automated testing and evaluation pipelines
  • red-teaming approaches for AI-generated systems
  • enterprise knowledge into machine-readable structures
  • embed AI sustainably into existing system landscapes
  • agentic AI
  • LLM orchestration
  • AI-native architectures
  • AI agents
  • agent assembly lines
  • execution harnesses
  • context engineering

Other signals

  • design agentic platforms
  • autonomous delivery pipelines
  • intelligent systems
  • AI-driven architectures
  • AI-based software factories
  • autonomous software development
  • AI-powered delivery pipelines
  • trusted AI engineering environments
  • automated testing and evaluation pipelines
  • red-teaming approaches for AI-generated systems
  • enterprise knowledge into machine-readable structures
  • embed AI sustainably into existing system landscapes
  • agentic AI
  • LLM orchestration
  • AI-native architectures
  • AI agents
  • agent assembly lines
  • execution harnesses
  • context engineering