SCON AI London
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Job Summary
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 AIincluding 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 in applying these techniques to real-world problems for tasks such as text code image and multimodal generation. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering structured outputs function/tool calling and orchestration frameworks like LangChain LangGraph LlamaIndex and Semantic Kernel. Hands-on exposure to both API-based (e.g. Claude GPT Gemini) and open-source (e.g. Llama Mistral) LLM-based solution design.
Agentic AI & Orchestration: Hands-on experience designing autonomous and multi-agent systems that reason plan and act using tools. Familiarity with agentic design patterns (e.g. ReAct planning reflection tool use human-in-the-loop) and agent frameworks such as LangGraph CrewAI MAF the OpenAI Agents SDK and Googles Agent Development Kit (ADK). Experience building agentic workflows with memory state management and reliable multi-step task execution.
Model Context Protocol (MCP) & Interoperability: Practical understanding of the Model Context Protocol (MCP) for standardized secure connectivity between LLMs/agents and external tools data sources and systems. Ability to build and consume MCP servers and clients and to work with MCP primitives such as tools resources and prompts. Awareness of related interoperability standards (e.g. agent-to-agent communication) for composing enterprise-grade agentic systems.
Agent Skills & Extensibility: Experience extending agent capabilities through modular reusable skillspackaged instructions scripts and resources (e.g. -style capability modules) that agents load on demand via progressive disclosure. Ability to design custom tools connectors and skills that let agents perform specialized domain-specific tasks reliably and safely.
Retrieval-Augmented Generation (RAG) & Knowledge Systems: Proven experience designing RAG and knowledge-grounded systems including chunking strategies embeddings vector databases (e.g. Pinecone Weaviate Chroma pgvector FAISS) hybrid search reranking and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding.
Technical Proficiency: An overall understanding of below technologies is required :
Machine learning algorithms: Linear regression logistic regression decision trees random forests support vector machines neural networks
Data science tools: NumPy SciPy Pandas Matplotlib TensorFlow Keras
Cloud computing platforms: AWS Azure GCP
Natural language processing (NLP): Transformer models attention mechanisms word embeddings
Computer vision: Convolutional neural networks recurrent neural networks object detection
Robotics: Reinforcement learning motion planning control systems
Data ethics: Bias in machine learning fairness in algorithms
Foundation models & LLMs: GPT Claude Gemini Llama Mistral; multimodal and reasoning models; context windows tokenization and fine-tuning (LoRA/PEFT) RLHF/RLAIF concepts
LLM application & agent frameworks: LangChain LangGraph LlamaIndex Semantic Kernel Haystack CrewAI AutoGen
Interoperability & integration: Model Context Protocol (MCP) function/tool calling structured outputs API integration event-driven and orchestration patterns
Cloud AI platforms & model hosting: Amazon Bedrock Azure OpenAI / AI Foundry Google Vertex AI Hugging Face
Vector databases & retrieval: Pinecone Weaviate Chroma pgvector FAISS; embeddings semantic and hybrid search reranking
MLOps / LLMOps & deployment: Docker Kubernetes FastAPI CI/CD; observability tracing and evaluation tooling (e.g. LangSmith LangFuse); guardrails and prompt/version management
Responsible AI & safety: bias and fairness hallucination mitigation evaluation privacy security and governance of AI and agentic systems
Solution Design: Ability to design end-to-end Generative and Agentic AI solutions from requirement elicitation and model selection to deployment strategy. Experience crafting architectures that encompass data preprocessing RAG pipelines agent orchestration MCP-based tool and system integration model integration guardrails and performance cost and latency optimization.
LLMOps Evaluation & Optimization: Experience operationalizing LLM and agentic applicationsbuilding evaluation harnesses and offline/online metrics for quality groundedness and safety; implementing observability tracing and monitoring; and continuously optimizing accuracy cost and latency. Familiarity with guardrails red-teaming and responsible deployment of AI systems in production.
Communication Skills: Excellent verbal and written communication skills to engage with clients articulate technical concepts to non-technical stakeholders and work collaboratively with cross-functional teams.
Secondary Skill Set:
Domain Knowledge: Familiarity with the industry domains in which the AI solutions will be applied. This includes understanding the specific challenges and requirements of different sectors such as healthcare finance or manufacturing.
Project Management: Basic project management skills to oversee project timelines milestones and deliverables. Experience in coordinating with internal teams and clients to ensure project success.
Data Understanding: A foundational grasp of data preprocessing feature engineering and data quality assurance processes. This aids in understanding the data requirements of AI models.
Responsible AI & Governance: Awareness of AI governance safety and compliance considerationsdata privacy security bias and fairness transparency and emerging AI regulationsand how they shape the design and deployment of enterprise Generative and Agentic AI solutions.
Roles & Responsibilities:
Client Interaction: Collaborate with client business teams to elicit project requirements and comprehend the desired outcomes. Translate client needs into technical requirements and AI solution designs.
Solution Design: Create comprehensive AI solution designs that address client objectives. Define the architecture model selection and data requirements to ensure successful project execution.
Agentic Solution Architecture: Architect Generative and Agentic AI solutionsselecting appropriate agent frameworks RAG strategies MCP-based integrations and skillsand define patterns for reliability safety human oversight and scalable production deployment.
Metrics Definition: Work closely with clients to define and agree upon measurable metrics that align with business goals. Ensure that the AI solutions 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 the solution effectively.
Performance Monitoring: Establish mechanisms to monitor and assess the performance of deployed AI models. Make recommendations for improvements based on observed outcomes.
Client Collaboration: Act as a liaison between the client and internal teams maintaining effective communication throughout the project lifecycle. Provide regular updates and address any concerns or queries from clients.
Personal
Besides the professional qualifications we respect and place equal importance to the candidates personality which facilitates success in customer environments. Few traits we look for are:
High analytical skills
A high degree of initiative flexibility and adaptability
High customer orientation
Good team engaging skills
Quality awareness
Good verbal and written communication skills
Transparency and Integrity
Taking accountability
About Topaz CoE
Topaz Centre of Excellence is the Central AI and automation evangelisation unit at Infosys. Our vision is to enable Infosys towards delivering exponential value to customers through AI driven differentiation across all horizontal and vertical services leveraging our internal as well as our partner capabilities. We enable Infosys towards an AI First Organization and to establish Infosys as a Market Leader in AI and Gen AI space. We help clients define roadmaps and realize productivity gains and business benefits through Automation AI and Gen AI. Our AI-first set of services solutions and platforms using generative AI technologies help amplify the potential of humans enterprises and communities to create value from unprecedented innovations pervasive efficiencies and connected ecosystems. We bring the advantage of 12000 AI assets 150 pre-trained AI models 10 AI platforms steered by AI-first specialists and data strategists and a responsible by design approach that is uncompromising on ethics trust privacy security and regulatory compliance.
About Infosys
Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.
With over four decades of experience in managing the systems and workings of global enterprises we expertly steer clients in 59 countries as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills expertise and ideas from our innovation ecosystem. We are deeply committed to being a well-governed environmentally sustainable organization where diverse talent thrives in an inclusive workplace.
All aspects of employment at Infosys are based on merit competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer
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 AIincluding 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 in applying these techniques to real-world problems for tasks such as text code image and multimodal generation. Conversant with modern Gen AI development techniques and tooling such as advanced prompt engineering structured outputs function/tool calling and orchestration frameworks like LangChain LangGraph LlamaIndex and Semantic Kernel. Hands-on exposure to both API-based (e.g. Claude GPT Gemini) and open-source (e.g. Llama Mistral) LLM-based solution design.
Agentic AI & Orchestration: Hands-on experience designing autonomous and multi-agent systems that reason plan and act using tools. Familiarity with agentic design patterns (e.g. ReAct planning reflection tool use human-in-the-loop) and agent frameworks such as LangGraph CrewAI MAF the OpenAI Agents SDK and Googles Agent Development Kit (ADK). Experience building agentic workflows with memory state management and reliable multi-step task execution.
Model Context Protocol (MCP) & Interoperability: Practical understanding of the Model Context Protocol (MCP) for standardized secure connectivity between LLMs/agents and external tools data sources and systems. Ability to build and consume MCP servers and clients and to work with MCP primitives such as tools resources and prompts. Awareness of related interoperability standards (e.g. agent-to-agent communication) for composing enterprise-grade agentic systems.
Agent Skills & Extensibility: Experience extending agent capabilities through modular reusable skillspackaged instructions scripts and resources (e.g. -style capability modules) that agents load on demand via progressive disclosure. Ability to design custom tools connectors and skills that let agents perform specialized domain-specific tasks reliably and safely.
Retrieval-Augmented Generation (RAG) & Knowledge Systems: Proven experience designing RAG and knowledge-grounded systems including chunking strategies embeddings vector databases (e.g. Pinecone Weaviate Chroma pgvector FAISS) hybrid search reranking and evaluation of retrieval quality. Familiarity with advanced patterns such as GraphRAG and agentic RAG to reduce hallucination and improve factual grounding.
Technical Proficiency: An overall understanding of below technologies is required :
Machine learning algorithms: Linear regression logistic regression decision trees random forests support vector machines neural networks
Data science tools: NumPy SciPy Pandas Matplotlib TensorFlow Keras
Cloud computing platforms: AWS Azure GCP
Natural language processing (NLP): Transformer models attention mechanisms word embeddings
Computer vision: Convolutional neural networks recurrent neural networks object detection
Robotics: Reinforcement learning motion planning control systems
Data ethics: Bias in machine learning fairness in algorithms
Foundation models & LLMs: GPT Claude Gemini Llama Mistral; multimodal and reasoning models; context windows tokenization and fine-tuning (LoRA/PEFT) RLHF/RLAIF concepts
LLM application & agent frameworks: LangChain LangGraph LlamaIndex Semantic Kernel Haystack CrewAI AutoGen
Interoperability & integration: Model Context Protocol (MCP) function/tool calling structured outputs API integration event-driven and orchestration patterns
Cloud AI platforms & model hosting: Amazon Bedrock Azure OpenAI / AI Foundry Google Vertex AI Hugging Face
Vector databases & retrieval: Pinecone Weaviate Chroma pgvector FAISS; embeddings semantic and hybrid search reranking
MLOps / LLMOps & deployment: Docker Kubernetes FastAPI CI/CD; observability tracing and evaluation tooling (e.g. LangSmith LangFuse); guardrails and prompt/version management
Responsible AI & safety: bias and fairness hallucination mitigation evaluation privacy security and governance of AI and agentic systems
Solution Design: Ability to design end-to-end Generative and Agentic AI solutions from requirement elicitation and model selection to deployment strategy. Experience crafting architectures that encompass data preprocessing RAG pipelines agent orchestration MCP-based tool and system integration model integration guardrails and performance cost and latency optimization.
LLMOps Evaluation & Optimization: Experience operationalizing LLM and agentic applicationsbuilding evaluation harnesses and offline/online metrics for quality groundedness and safety; implementing observability tracing and monitoring; and continuously optimizing accuracy cost and latency. Familiarity with guardrails red-teaming and responsible deployment of AI systems in production.
Communication Skills: Excellent verbal and written communication skills to engage with clients articulate technical concepts to non-technical stakeholders and work collaboratively with cross-functional teams.
Secondary Skill Set:
Domain Knowledge: Familiarity with the industry domains in which the AI solutions will be applied. This includes understanding the specific challenges and requirements of different sectors such as healthcare finance or manufacturing.
Project Management: Basic project management skills to oversee project timelines milestones and deliverables. Experience in coordinating with internal teams and clients to ensure project success.
Data Understanding: A foundational grasp of data preprocessing feature engineering and data quality assurance processes. This aids in understanding the data requirements of AI models.
Responsible AI & Governance: Awareness of AI governance safety and compliance considerationsdata privacy security bias and fairness transparency and emerging AI regulationsand how they shape the design and deployment of enterprise Generative and Agentic AI solutions.
Roles & Responsibilities:
Client Interaction: Collaborate with client business teams to elicit project requirements and comprehend the desired outcomes. Translate client needs into technical requirements and AI solution designs.
Solution Design: Create comprehensive AI solution designs that address client objectives. Define the architecture model selection and data requirements to ensure successful project execution.
Agentic Solution Architecture: Architect Generative and Agentic AI solutionsselecting appropriate agent frameworks RAG strategies MCP-based integrations and skillsand define patterns for reliability safety human oversight and scalable production deployment.
Metrics Definition: Work closely with clients to define and agree upon measurable metrics that align with business goals. Ensure that the AI solutions 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 the solution effectively.
Performance Monitoring: Establish mechanisms to monitor and assess the performance of deployed AI models. Make recommendations for improvements based on observed outcomes.
Client Collaboration: Act as a liaison between the client and internal teams maintaining effective communication throughout the project lifecycle. Provide regular updates and address any concerns or queries from clients.
Personal
Besides the professional qualifications we respect and place equal importance to the candidates personality which facilitates success in customer environments. Few traits we look for are:
High analytical skills
A high degree of initiative flexibility and adaptability
High customer orientation
Good team engaging skills
Quality awareness
Good verbal and written communication skills
Transparency and Integrity
Taking accountability
About Topaz CoE
Topaz Centre of Excellence is the Central AI and automation evangelisation unit at Infosys. Our vision is to enable Infosys towards delivering exponential value to customers through AI driven differentiation across all horizontal and vertical services leveraging our internal as well as our partner capabilities. We enable Infosys towards an AI First Organization and to establish Infosys as a Market Leader in AI and Gen AI space. We help clients define roadmaps and realize productivity gains and business benefits through Automation AI and Gen AI. Our AI-first set of services solutions and platforms using generative AI technologies help amplify the potential of humans enterprises and communities to create value from unprecedented innovations pervasive efficiencies and connected ecosystems. We bring the advantage of 12000 AI assets 150 pre-trained AI models 10 AI platforms steered by AI-first specialists and data strategists and a responsible by design approach that is uncompromising on ethics trust privacy security and regulatory compliance.
About Infosys
Infosys is a global leader in next-generation digital services and consulting. We enable clients in 59 countries to navigate their digital transformation.
With over four decades of experience in managing the systems and workings of global enterprises we expertly steer clients in 59 countries as they navigate their digital transformation powered by cloud and AI. We enable them with an AI-first core empower the business with agile digital at scale and drive continuous improvement with always-on learning through the transfer of digital skills expertise and ideas from our innovation ecosystem. We are deeply committed to being a well-governed environmentally sustainable organization where diverse talent thrives in an inclusive workplace.
All aspects of employment at Infosys are based on merit competence and performance. We are committed to embracing diversity and creating an inclusive environment for all employees. Infosys is proud to be an equal opportunity employer
About Company
Welcome to Infosys Careers for Experienced Professionals As a leading provider of next-generation consulting, technology and outsourcing solutions, we are dedicated to helping organizations in over 46 countries to renew their core and simultaneously innovate into new frontiers. Whilst ... View more
