AI Architect

Role: AI Architect

Location: Oslo, Norway

Type: Permanent/ Full-Time

Primary Skill Set

Generative AI Expertise

• Deep expertise in modern Generative AI techniques and foundation models, including transformer-based Large Language Models (LLMs), diffusion models, multimodal models, GANs, and VAEs.
• Experience designing solutions across text, code, image, and multimodal generation.
• Strong knowledge of modern GenAI development practices, including prompt engineering, structured outputs, function/tool calling, and AI orchestration.
• Hands-on experience with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, and Semantic Kernel.
• Experience designing solutions using both commercial/API-based and open-source LLMs.

Agentic AI & Multi-Agent Architecture

• Expertise in designing autonomous and multi-agent systems capable of reasoning, planning, tool use, and action execution.
• Strong understanding of agentic design patterns including ReAct, planning, reflection, tool use, human-in-the-loop, and workflow-based orchestration.
• Experience with agent frameworks such as LangGraph, CrewAI, Microsoft Agent Framework (MAF), OpenAI Agents SDK, and Google Agent Development Kit (ADK).
• Ability to architect reliable agentic workflows incorporating memory, state management, orchestration, guardrails, and controlled multi-step execution.

Model Context Protocol (MCP) & Interoperability

• Strong working knowledge of Model Context Protocol (MCP) for standardized connectivity between AI agents, tools, data sources, and enterprise systems.
• Experience designing, implementing, and governing MCP clients and servers.
• Familiarity with MCP primitives such as tools, resources, and prompts.
• Understanding of emerging interoperability standards and agent-to-agent communication patterns for scalable AI ecosystems.

Agent Skills & Extensibility

• Experience developing modular, reusable agent capabilities through skills, instructions, scripts, and supporting resources.
• Ability to design capability modules that can be dynamically loaded through progressive disclosure.
• Experience defining standards for custom tools, connectors, and reusable skills.
• Focus on enabling agents to perform specialized tasks consistently, securely, and reliably across different domains and teams.

Retrieval-Augmented Generation & Knowledge Architecture

• Expertise in architecting RAG and knowledge-grounded AI systems.
• Strong understanding of document chunking, embeddings, vector databases, hybrid search, reranking, retrieval optimization, and evaluation.
• Experience with vector technologies such as Pinecone, Weaviate, Chroma, pgvector, and FAISS.
• Familiarity with advanced approaches including GraphRAG and agentic RAG.
• Ability to design retrieval architectures that improve factual grounding, relevance, and reliability.

LLMOps, Evaluation & Responsible AI

• Experience operationalizing LLM and agentic AI systems for production environments.
• Expertise in evaluation frameworks and metrics covering quality, groundedness, safety, reliability, and task completion.
• Familiarity with observability, tracing, monitoring, and debugging tools such as LangSmith and Langfuse.
• Experience implementing guardrails, testing strategies, red-teaming, and continuous optimization.
• Understanding of AI governance, security, privacy, fairness, auditability, and evolving AI regulations.
• Ability to balance model quality with cost, latency, scalability, and operational requirements.

Machine Learning

• Strong understanding of machine learning principles, algorithms, model development, optimization, and training methodologies.
• Ability to design, implement, evaluate, and optimize machine learning models and pipelines.

Technical Proficiency

• Strong programming experience in Python and familiarity with AI/ML frameworks such as PyTorch, TensorFlow, or equivalent technologies.
• Experience with modern LLM and agent frameworks including LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, and AutoGen.
• Familiarity with cloud AI platforms such as Amazon Bedrock, Azure AI, and Google Vertex AI.
• Experience with vector databases, containerization technologies such as Docker, Kubernetes, and distributed computing environments.

Architecture & Solution Design

• Ability to design end-to-end Generative and Agentic AI architectures spanning data ingestion, preprocessing, model selection, RAG, agent orchestration, MCP-based integrations, guardrails, inference, observability, and deployment.
• Strong understanding of scalable, secure, reliable, and cost- and latency-efficient architecture principles.
• Ability to translate business and technical requirements into production-ready AI architectures and reusable platform patterns.

Secondary Skill Set

Domain Knowledge

• Familiarity with the business domain or industry in which AI solutions are deployed.
• Ability to apply domain knowledge to identify relevant use cases and design context-aware AI solutions.

Data Engineering

• Understanding of data engineering principles, data pipelines, data quality, and data management.
• Experience with data preprocessing, cleansing, transformation, and preparation for AI/ML workloads.

AI Governance, Security & Responsible AI

• Understanding of AI governance, security, safety, compliance, privacy, fairness, transparency, and auditability.
• Ability to incorporate responsible AI principles into the architecture and lifecycle of enterprise AI solutions.

Communication & Collaboration

• Strong communication and collaboration skills across engineering, data science, product, architecture, and business teams.
• Ability to communicate complex AI concepts clearly to both technical and non-technical stakeholders.
• Experience mentoring engineers and architects and contributing to technical standards and best practices.

Roles & Responsibilities

Generative & Agentic AI Strategy

• Define and contribute to Generative and Agentic AI technology strategies and roadmaps.
• Identify high-value AI opportunities and evaluate potential use cases.
• Translate business objectives into practical, scalable AI solutions.

Model & Technology Selection

• Evaluate and select appropriate foundation models, agent frameworks, RAG approaches, integration patterns, and interoperability standards.
• Assess technology choices based on requirements such as data availability, complexity, quality, safety, cost, latency, scalability, and computational requirements.

Architectural Design

• Design scalable, secure, reliable, and production-ready Generative AI architectures.
• Define architectures covering data ingestion, preprocessing, model integration, retrieval, inference, deployment, monitoring, and governance.

Agentic AI & Platform Architecture

• Establish reusable architecture patterns and platform standards for agentic AI.
• Define approaches for agent orchestration, tool integration, MCP connectivity, shared skills, connectors, memory, state management, guardrails, human oversight, and observability.
• Enable consistent and scalable adoption of agentic AI across development teams.

Solution Implementation

• Collaborate with data scientists, software engineers, platform teams, and other stakeholders to implement AI solutions.
• Ensure AI capabilities are effectively integrated into existing applications, platforms, and enterprise systems.
• Guide the transition of AI prototypes and proofs of concept into production-grade solutions.

Performance & Optimization

• Continuously optimize AI solutions for quality, accuracy, latency, scalability, reliability, and cost.
• Analyze system and model performance and implement improvements based on evaluation results and production feedback.

Evaluation & Continuous Improvement

• Define success criteria, evaluation methodologies, and measurable outcomes for AI solutions.
• Assess solution performance against established metrics and continuously refine models, prompts, retrieval strategies, agent workflows, and architectures.

Stakeholder Collaboration

• Collaborate with business, architecture, engineering, and product stakeholders to understand requirements and define technical boundaries.
• Translate business needs into appropriate AI architecture and implementation strategies.
• Establish appropriate technical expectations around scalability, security, reliability, and service requirements.

Team Leadership & Mentorship

• Provide technical guidance and mentorship to engineers, data scientists, and architects.
• Promote reusable patterns, engineering standards, documentation, and best practices.
• Foster collaboration and knowledge sharing across AI and engineering teams.

Industry & Technology Awareness

• Stay current with rapidly evolving developments in Generative AI, Agentic AI, foundation models, agent frameworks, MCP, AI skills, interoperability standards, and related technologies.
• Evaluate emerging technologies and determine their applicability to enterprise AI architectures.
• Share technical insights and contribute to the evolution of AI engineering and architecture practices.