AI Engagement Senior Architect

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