GenAI Solutions Architect

About The Role

We're looking for an experienced and personable GenAI Solutions Architect to join our growing AI delivery team. You'll design and build large language model (LLM) systems that move beyond experimentation and into real-world production-powering search, summarisation, knowledge assistants, and automation for enterprise clients. This is a hands on, execution focused role that requires strong stakeholder management skills. You'll work closely with product managers, engineers, and AI specialists to ship scalable solutions. You won't be buried in research or building theoretical models; you'll be deploying actual systems that users rely on every day.

Responsibilities

• Architect end to end GenAI systems, including prompt chaining, memory strategies, token budgeting, and embedding pipelines.
• Design and optimise RAG (Retrieval Augmented Generation) workflows using tools like LangChain, LlamaIndex, and vector databases (FAISS, Pinecone, Qdrant). Experience with Graph RAG architectures and proficiency in Node.js required.
• Evaluate trade offs between zero shot prompting, fine tuning, LoRA/QLoRA, and hybrid approaches, aligning solutions with user goals and constraints.
• Integrate LLMs and APIs (OpenAI, Anthropic, Cohere, Hugging Face) into real time products and services with latency, scalability, and observability in mind.
• Collaborate with cross functional teams-translating complex GenAI architectures into stable, maintainable features that support product delivery.
• Write and review technical design documents and remain actively involved in implementation decisions.
• Deploy to production with industry best practices around version control, API lifecycle management, and monitoring (e.g., hallucination detection, prompt drift).

What You'll Bring

• Proven background in Machine Learning.
• Proven experience building and deploying GenAI powered applications in enterprise or regulated environments.
• Deep understanding of LLMs, vector search, embeddings, and GenAI design patterns (e.g., RAG, prompt injection protection, tool use with agents).
• Proficiency in Python and fluency with frameworks and libraries like LangChain, Transformers, Hugging Face, and OpenAI SDKs.
• Experience with vector databases such as FAISS, Qdrant, or Pinecone.
• Familiarity with cloud infrastructure (AWS, GCP, or Azure) and core MLOps concepts (CI/CD, monitoring, containerisation).
• Proven experience supporting and/or delivering AI/ML products.
• A commercial and delivery mindset-you know how to balance speed, quality, and feasibility in fast moving projects.

Nice to Have

• Experience building multi tenant GenAI platforms.
• Exposure to enterprise grade AI governance and security standards.
• Familiarity with multimodal architectures (e.g., text + image or audio).
• Knowledge of cost optimization strategies for LLM inference and token usage.

This Role Is Not For

• ML researchers focused on academic model development without delivery/product experience.
• Data scientists unfamiliar with vector search, LLM prompt engineering, or system architecture.
• Engineers who haven't shipped GenAI products into production environments.

Benefits & Growth Opportunities

• Competitive salary and performance bonuses.
• Comprehensive health insurance.
• Professional development and certification support.
• Opportunity to work on cutting edge AI projects.
• International exposure and travel opportunities.
• Flexible working arrangements.
• Career advancement opportunities in a rapidly growing AI company.

Seniority Level

• Director

Employment Type

• Full time

Job Function

• Other

Industries

• IT Services and IT Consulting