Infinity Quest
Gen AI Architect
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Role: Senior Generative AI Architect
Role Location: London, UK (Hybrid) (3 Days onsite in a week is Mandatory)
Duration: Permanent Position
Skills: AI/ML architecture; AI frameworks; AI models & solutions; cloud platforms (e.g., AWS, Azure, Google Cloud) and programming languages such as Python, Java, or C++.
Job Description:
The Senior Generative AI Architect will be responsible for designing, developing, and implementing generative AI solutions that align with the company's strategic objectives. This role involves leading the architecture and deployment of advanced AI models, ensuring scalability, security, and ethical considerations are integrated into all AI initiatives. The ideal candidate will possess deep expertise in generative AI technologies, a strong understanding of AI ethics, and the ability to collaborate effectively with cross-functional teams.
Key Responsibilities:
- Architecture Design: Develop and maintain the architectural framework for generative AI solutions, ensuring alignment with business goals and technical standards.
- Model Development: Lead the design, training, and deployment of generative AI models (e.g., GPT, DALL-E, Stable Diffusion) tailored to various applications such as content generation, data synthesis, and automation.
- Integration: Collaborate with software engineering teams to integrate generative AI capabilities into existing systems and workflows.
- Scalability & Performance: Ensure AI solutions are scalable, efficient, and optimized for performance across different platforms and environments.
- Ethical AI Practices: Implement and enforce ethical guidelines for AI development and deployment, addressing issues such as bias, fairness, and transparency.
- Research & Innovation: Stay abreast of the latest advancements in generative AI and related fields, incorporating new techniques and tools into the company's AI strategy.
- Collaboration: Work closely with data scientists, engineers, product managers, and other stakeholders to identify opportunities for AI-driven solutions and ensure successful project delivery.
- Documentation & Standards: Create comprehensive documentation for AI architectures, processes, and best practices. Establish and maintain coding and architectural standards.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Experience:
- Minimum of 7 years of experience in AI/ML architecture or a related role.
- Proven experience in designing and deploying generative AI models and solutions.
- Hands-on experience with AI frameworks and tools such as TensorFlow, PyTorch, Hugging Face, etc.
- Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and deploying AI solutions in cloud environments.
- Technical Skills: Proficiency in programming languages such as Python, Java, or C++.
- Strong understanding of machine learning algorithms, deep learning
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Core AI / RAG Topics
- RAG Architecture & Design (end-to-end, scaling, governance)
- Chunking Strategy (token limits, overlap, semantic chunking)
- Retrieval Techniques (Vector vs Hybrid vs BM25, recall vs precision)
- Improving Answer Quality (re-ranking, prompts, context filtering, model selection)
- RAG Evaluation Metrics (Recall@K, Precision@K, faithfulness, hallucination)
- Hallucination Control (grounding, citations, retrieval quality)
- Cost & Performance Optimization (token reduction, caching, efficient models)
- Agents & Orchestration
- LangChain vs LangGraph (stateful workflows, long-running agents)
- State Management (shared state, transitions, persistence)
- Multi-Agent Communication (message passing, structured outputs)
- Advanced & Enterprise Topics
- Vector RAG vs Knowledge Graph RAG (relationships, ambiguity handling)
- Failure Handling (retry, fallback, validation, human-in-loop)
- Enterprise Security (RBAC, tool restrictions, prompt injection protection, audit logs)
- Practical Experience Validation
- Recent AI/LLM Project Experience (production, scale, ownership)
- RAG & Vector DB Usage (Azure AI Search, Pinecone, FAISS, etc.)
- Hands-on Coding (Python, APIs, backend)
- Tools & Frameworks (LangChain, LangGraph, orchestration tools)
- Challenges Faced (latency, cost, hallucination, scaling)
- Decision Making (cost vs performance vs scalability)
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location