At a Glance
- Tasks: Lead the design of innovative AI architectures and develop cutting-edge solutions.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative culture with a focus on continuous learning and development.
- Why this job: Shape the future of AI while working on impactful projects in a dynamic environment.
- Qualifications: Expertise in AI technologies, programming, and machine learning principles required.
The predicted salary is between 80000 - 100000 £ per year.
Role AI Evangelist (Senior Technology Architect) Technology, Location advanced prompt engineering; orchestration frameworks (Lang Chain, Lang Graph, Llama Index, Semantic Kernel).
Hands‑on exposure to both API‑based (Claude, GPT, Gemini) and open‑source (Llama, Mistral) LLM‑based solution design.
Agentic AI ability to build and govern MCP servers and clients, and familiarity with related interoperability standards.
Agent Skills defining standards for custom tools, connectors and skills ensuring reliable, secure, and consistent operation.
Retrieval‑Augmented Generation (RAG) evaluation harnesses, quality metrics, observability, tracing and monitoring (Lang Smith, Lang Fuse); guardrails, red‑teaming and continuous optimization of accuracy, cost and latency.
Understanding of AI governance, security, privacy, bias/fairness and emerging regulations.
Machine Learning Mastery – profound understanding of ML principles, algorithms, frameworks, and training pipelines.
Technical Proficiency – programming in Python, Tensor Flow, Py Torch or similar; modern LLM/agent frameworks; cloud AI platforms (Amazon Bedrock, Azure Open AI / AI Foundry, Google Vertex AI); vector databases; containerization and orchestration (Docker, Kubernetes); distributed computing.
Architecture Design – end‑to‑end design of Generative and Agentic AI architectures encompassing data preprocessing, model selection, RAG pipelines, agent orchestration, MCP‑based integration, guardrails, training/inference pipelines and deployment strategies.
Secondary Skill Set Domain Knowledge – familiarity with the specific industry domain (e. g., healthcare, finance, entertainment) to enable contextual and tailored solutions.
Data Engineering – understanding of data pipelines, preprocessing, cleansing and transformation for model training.
AI Governance, Security
