Job Specifications
Job Description – AI Engagement Senior Architect
Role: AI Engagement Senior Architect
Skills: GenAI, Agentic AI, ML, LLMs, GCP Architecture
Location: London or Sheffield
Experience: 12–18 years (with 4–6 years in AI/GenAI)
Role Summary
We are seeking an AI Engagement Senior Architect to lead GenAI solutioning, architecture design, and client engagement
across enterprise AI programs. The role involves driving AI strategy, designing scalable GenAI and Agentic AI systems on
GCP, guiding engineering teams, and ensuring end-to-end delivery quality. The ideal candidate is both a hands-on
technologist and a client-facing architectural leader.
Key Responsibilities
AI Strategy & Solution Architecture
• Define AI/GenAI solution architecture using LLMs, Agentic frameworks, multimodal models, and ML systems.
• Lead architecture workshops with CXO, Product, and Engineering stakeholders.
• Translate business challenges into AI/ML use cases, PoVs, PoCs, and scalable roadmaps.
• Own end-to-end GenAI system design: data pipelines → vector DB → LLM orchestration → evaluation →
Deployment.
Technical Leadership
Architect and implement GenAI solutions using RAG, agents, tool-use, prompt engineering, fine-tuning, model evaluation, and guardrails.
Build agentic automation workflows using frameworks such as LangChain, LlamaIndex, Haystack, CrewAI, Vertex AI Agent Builder, etc.
Lead ML engineering best practices (MLOps, LLMOps, CI/CD for AI workloads).
Ensure enterprise-grade security, governance, privacy, and model risk controls.
GCP Architecture & Cloud Engineering
Design scalable AI workloads on Google Cloud using:
Vertex AI, BigQuery, GCS, Cloud Run, Dataflow, Pub/Sub, Looker, GKE
Architect hybrid GenAI solutions using 1P + open-source + 3P model ecosystems.
Drive cost-efficient cloud architectures and performance optimization.
Client Engagement & Delivery
Act as the technical face for all AI engagements.
Support pre-sales, RFP responses, solution proposals, and estimations.
Guide cross-functional engineering teams towards successful delivery.
Conduct technical reviews, architectural assessments, and maturity evaluations.
Must-Have Skills (with proficiency levels)
Skill Area
Required Expertise
GenAI Architecture Expert in RAG, LLM-based systems, prompt engineering, embeddings, guardrails
Agentic AI
Hands-on with agent frameworks, tool invocation, autonomous workflows
LLMs
Experience with GPT, Gemini, Llama, Claude, and fine-tuning/safety/measuring
ML Engineering
Strong in supervised/unsupervised ML, pipelines, feature engineering
GCP Architecture
Deep expertise with Vertex AI, GCS, BigQuery, GKE, Dataflow
MLOps/LLMOps
CI/CD, model registry, monitoring, evaluation frameworks
Programming
Python is mandatory; familiarity with REST/GraphQL APIs
Vector Databases Knowledge of Pinecone, Chroma, Weaviate, Vertex Vector Search
Nice-to-Have Skills
Experience with enterprise AI patterns (retrieval pipelines, agents, copilots, AI apps)
Knowledge of AWS or Azure AI stacks
Experience implementing GDPR/PII controls in AI systems
Exposure to data engineering (ETL, data modeling, batch/stream processing)
Hands-on with experiment tracking (MLflow, Vertex AI Experiments)
Educational Background
Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related fields
Cloud Architect certifications (preferred):
o Google Professional Cloud Architect
o Google Machine Learning Engineer