At a Glance
- Tasks: Design and implement innovative AI solutions for diverse clients.
- Company: Leading tech firm in London focused on AI advancements.
- Benefits: Competitive salary, bonuses, and opportunities for professional growth.
- Other info: Dynamic work environment with exciting projects and career progression.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: Strong expertise in Generative AI and excellent communication skills.
The predicted salary is between 80000 - 100000 £ per year.
Role Overview
- Role: AI Evangelist (Senior Technology Architect)
- Technology: AI/ML/Gen AI, Data Science, Poly Cloud – Azure, AWS, GCP
- Location: London – UK
- Business Unit: TOPAZDLVRY
- Compensation: Competitive (including bonus)
Job Summary
We are seeking an accomplished Generative AI Consultant to drive the design and implementation of innovative AI solutions for our clients. The Generative AI Consultant will play a critical role in understanding client needs, designing tailored solutions, and ensuring the successful delivery of projects that meet defined metrics. This role requires strong technical expertise across Generative and Agentic AI—including LLMs, retrieval-augmented generation (RAG), autonomous and multi-agent systems, and modern interoperability standards such as the Model Context Protocol (MCP)—coupled with excellent communication skills to engage with clients and internal teams effectively.
Primary Skill Set
- Generative AI Expertise: Good understanding of modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, and multimodal models, as well as earlier architectures such as GANs and VAEs. Proven experience applying these techniques to real-world tasks (text, code, image, multimodal generation) and familiarity with advanced prompt engineering, structured outputs, function/tool calling, and orchestration frameworks like LangChain, LangGraph, LlamaIndex, and Semantic Kernel. Hands-on exposure to API-based LLM providers (Claude, GPT, Gemini) and open-source solutions (Llama, Mistral).
- Agentic AI: multimodal and reasoning models; context windows, tokenization, fine-tuning (LoRA/PEFT); RLHF/RLAIF concepts; LLM application embeddings, semantic and hybrid search, reranking; MLOps / LLMOps observability, tracing, evaluation tooling (LangSmith, LangFuse); guardrails, prompt/version management.
- Responsible AI: implementing observability, tracing, and monitoring; continuously optimizing accuracy, cost, and latency. Familiarity with guardrails, red-team, and responsible deployment of AI systems in production.
- Communication Skills: Excellent verbal and written communication to engage with clients, articulate technical concepts to non-technical stakeholders, and collaborate with cross-functional teams.
Secondary Skill Set
- Domain Knowledge: Familiarity with industry domains (healthcare, finance, manufacturing) and specific challenges and requirements of AI solutions in those sectors.
- Project Management: Basic project management skills, overseeing timelines, milestones, and deliverables; coordinating internal teams and clients to ensure project success.
- Data Understanding: Foundational grasp of data preprocessing, feature engineering, and data quality assurance processes to support AI model requirements.
- Responsible AI: translate needs into technical requirements and AI solution designs.
- Solution Design: Create comprehensive AI solution designs addressing client objectives; define architecture, model selection, and data requirements for successful execution.
- Agentic Solution Architecture: Architect Generative and Agentic AI solutions—select appropriate frameworks, RAG strategies, MCP-based integrations, and skills; define patterns for reliability, safety, human oversight, and scalable production deployment.
- Metrics Definition: Work closely with clients to define and agree on measurable metrics aligned with business goals; ensure AI solution performance is evaluated against these metrics.
- Technical Implementation: Provide guidance to internal teams on implementing the defined AI solution; collaborate with data scientists and engineers to integrate effectively.
- Performance Monitoring: Establish mechanisms to monitor and assess performance of deployed AI models; recommend improvements based on observed outcomes.
- Client Collaboration: Act as liaison between client and internal teams, maintaining effective communication throughout the project lifecycle; provide regular updates and address client queries.
Personal Qualities
Senior AI Engineer| London employer: Infosys Limited
At Infosys, we pride ourselves on being an exceptional employer, particularly for the Quality Engineering Lead role in Leeds. Our vibrant work culture fosters collaboration and innovation, while our commitment to employee growth ensures that you will have ample opportunities to develop your skills and advance your career. With competitive compensation, including bonuses, and a focus on diversity and inclusion, we create a rewarding environment where you can thrive both personally and professionally.
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We think you need these skills to ace Senior AI Engineer| London
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