Key Responsibilities
- Define and own the enterprise architecture for Agentic AI platforms, AI assistants, copilots, and autonomous workflows.
- Design and implement multi-agent systems leveraging reasoning, planning, memory, orchestration, and tool integration.
- Architect production-grade GenAI solutions including RAG, intelligent search, knowledge assistants, workflow automation, and decision-support systems.
- Establish AI engineering standards covering LLMOps, MLOps, platform governance, observability, security, and responsible AI.
- Lead technology selection across LLMs, orchestration frameworks, vector databases, and cloud AI platforms.
- Drive enterprise adoption of AI while ensuring scalability, security, compliance, and commercial viability.
- Provide technical leadership to architects, engineers, data scientists, and senior business stakeholders.
- Partner with executive leadership to define AI strategy, roadmaps, investment priorities, and innovation opportunities.
Technical Skills
Agentic AI & GenAI
Multi-Agent Systems
Agentic AI Architecture
Retrieval Augmented Generation (RAG)
LLM Application Design
Prompt Engineering
Responsible AI
AI Evaluation Frameworks
LangChain, LangGraph, Semantic Kernel, AutoGen
Cloud & AI Platforms
Azure AI Foundry / Azure OpenAI
AWS Bedrock & SageMaker
Google Vertex AI
Hybrid and Multi-Cloud Architectures
Engineering & Platform
Python, Java, Scala, TypeScript, SQL
Kubernetes, Docker
Terraform, Bicep
CI/CD and Infrastructure as Code
GitHub Actions, GitLab, Jenkins
AI Governance & Observability
LLMOps & MLOps
Model Governance
AI Security & Guardrails
LangSmith
Arize
Monitoring & Evaluation Frameworks
Rate:
Not specified
Location:
London
IR35 Status:
Inside
Remote Status:
Hybrid
Industry:
AI & Machine Learning
Seniority Level:
Not Specified