STAAI ArchitectOslo

Role AI Evangelist (Senior Technology Architect)
Technology AI/ ML/Gen AI Data Science Poly Cloud Azure AWS GCP
Location Oslo Norway
Business Unit TOPAZDLVRY
Compensation Competitive (including bonus)

Job Summary: We are seeking a highly skilled and experienced Senior Architect/Consultants to lead our Generative AI Technologies team. The ideal candidate will have a deep understanding of Generative and Agentic AI LLMs retrieval-augmented generation (RAG) machine learning and modern interoperability standards such as the Model Context Protocol (MCP) along with a proven track record of architecting and implementing innovative enterprise-scale solutions. As a Senior Architect/Consultant you will play a pivotal role in shaping our Generative AI strategy selecting appropriate models and technologies and collaborating with cross-functional teams to deliver cutting-edge solutions that meet customer requirements and business objectives.

Primary Skill Set:
Generative AI Expertise: In-depth knowledge 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 (Generative Adversarial Networks) and VAEs (Variational Autoencoders). Experience across text code image and multimodal generation is essential. 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 & Multi-Agent Architecture: Deep expertise designing autonomous and multi-agent systems that reason plan and act using tools. Command of 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). Proven ability to architect reliable agentic workflows with memory state management orchestration and safe multi-step task execution at scale.
Model Context Protocol (MCP) & Interoperability: Strong working knowledge of the Model Context Protocol (MCP) for standardized secure connectivity between LLMs/agents and enterprise tools data sources and systems. Ability to architect build and govern 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 scalable enterprise-grade agentic ecosystems.
Agent Skills & Extensibility: Experience extending agent capabilities through modular reusable skillspackaged instructions scripts and resources (e.g. -style capability modules) loaded on demand via progressive disclosure. Ability to define standards for custom tools connectors and skills that let agents perform specialized domain-specific tasks reliably securely and consistently across teams.
Retrieval-Augmented Generation (RAG) & Knowledge Architecture: Expertise architecting RAG and knowledge-grounded systemschunking strategies embeddings vector databases (e.g. Pinecone Weaviate Chroma pgvector FAISS) hybrid search reranking and retrieval evaluation. Familiarity with advanced patterns such as GraphRAG and agentic RAG to maximize factual grounding and minimize hallucination in production.
LLMOps Evaluation & Responsible AI: Experience operationalizing LLM and agentic systems at scaleevaluation harnesses and metrics for quality groundedness and safety; observability tracing and monitoring (e.g. LangSmith LangFuse); guardrails and red-teaming; and continuous optimization of accuracy cost and latency. Understanding of AI governance security privacy bias/fairness and emerging AI regulation.
Machine Learning Mastery: Profound understanding of machine learning principles algorithms and frameworks. Able to design and implement models optimize performance and manage training pipelines effectively.
Technical Proficiency: Proficiency in programming languages commonly used in AI development such as Python TensorFlow PyTorch or similar tools along with modern LLM/agent frameworks (LangChain LangGraph LlamaIndex Semantic Kernel CrewAI AutoGen). Experience with cloud AI platforms (e.g. Amazon Bedrock Azure OpenAI / AI Foundry Google Vertex AI) vector databases (e.g. Pinecone Weaviate Chroma pgvector FAISS) containerization and orchestration (Docker Kubernetes) and distributed computing is advantageous.
Architecture Design: Ability to design end-to-end Generative and Agentic AI architectures that encompass data preprocessing model selection RAG pipelines agent orchestration MCP-based tool and system integration guardrails training/inference pipelines and deployment strategies. Strong grasp of scalable reliable secure and cost- and latency-efficient system design for enterprise-grade AI.

Secondary Skill Set:
Domain Knowledge: Familiarity with the specific industry domain or vertical in which the Generative AI solutions will be applied (e.g. healthcare finance entertainment) is beneficial. This enables contextual understanding and tailored solution development.
Data Engineering: Understanding of data engineering practices data pipelines and data management. Proficiency in data preprocessing cleansing and transformation for effective model training.
AI Governance Security & Responsible AI: Familiarity with AI governance safety and compliance considerationsdata privacy security bias and fairness transparency auditability and emerging AI regulationsand how they shape the architecture and deployment of enterprise Generative and Agentic AI solutions.
Communication Skills: Excellent communication and collaboration skills to effectively interface with cross-functional teams including data scientists engineers business stakeholders and customers. Ability to convey complex technical concepts to non-technical stakeholders.
Roles & Responsibilities:
Generative AI Strategy: Lead the development of the Generative and Agentic AI technology roadmapidentifying opportunities evaluating potential use cases and proposing innovative agent-driven solutions that align with business goals.
Model Selection: Evaluate and select appropriate models agent frameworks RAG strategies and integration standards (including MCP) based on the specific requirements of each project. Consider factors such as data availability complexity safety cost latency and computational resources.
Architectural Design: Design comprehensive and scalable architectures for Generative AI solutions considering components such as data preprocessing model training deployment and monitoring.
Agentic & Platform Architecture: Define reusable architecture patterns and platform standards for agentic AIagent orchestration MCP-based tool/data integration shared skills and connectors memory and state management guardrails human oversight and observabilityto enable safe reliable and scalable production deployment across teams.
Solution Implementation: Collaborate with data scientists and engineers to implement Generative AI solutions ensuring the seamless integration of models into production environments.
Performance Optimization: Continuously optimize the performance of Generative AI models addressing issues related to speed accuracy and resource utilization.
Outcome Review: Assess the outcomes of Generative AI solutions against predefined success criteria. Iterate on models and strategies based on performance metrics and feedback.
Customer Collaboration: Work closely with customer architecture and business teams to define solution requirements technical boundaries and SLAs. Tailor solutions to meet customer needs and address specific challenges.
Team Collaboration: Collaborate effectively with cross-functional teams providing guidance and mentorship to junior team members. Foster a collaborative and innovative work environment.
Industry Awareness: Stay updated with the fast-evolving Generative and Agentic AI landscapenew models agent frameworks MCP skills and related technologies. Share insights with the team and incorporate emerging trends into solution and platform architecture.

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