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AI Architect
Axis Communications. Design secure, scalable and resilient enterprise-grade AI architectures across generative AI, machine learning, intelligent automation and agent-based use cases .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing secure, scalable AI architectures and integrating AI solutions with enterprise systems. Proficient in MLOps, responsible AI principles, and stakeholder management to drive AI governance and deliver measurable business value.
Highest-signal resume keywords
AI Architecture DesignMLOpsGenerative AICloud PlatformsStakeholder Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI ArchitectureMachine LearningData IntegrationSoftware EngineeringSecurityPerformance OptimizationGovernanceCost ControlModel EvaluationPrompt Design
Soft Skills
Clear CommunicationCollaborationFacilitationCuriosityJudgement
Tools & Technologies
DatabricksAzureAzure AI FoundryAzure OpenAICopilot StudioMicrosoft Fabric
Certifications & Qualifications
Cloud CertificationsArchitecture CertificationsData CertificationsSecurity CertificationsAI Certifications
Industry Keywords
InsuranceReinsuranceFinancial ServicesAI GovernanceRisk Management
Tech Stack
Tools & technologiesAzureCloud
About the role
Key responsibilities & impact- Design secure, scalable and resilient enterprise-grade AI architectures across generative AI, machine learning, intelligent automation and agent-based use cases
- Translate business needs into AI designs and explain technical choices, benefits, limitations, risks and trade-offs
- Define reusable AI architecture patterns, shared components, standards and guardrails
- Identify common requirements and shape reusable components, services and reference implementations
- Maintain architectural oversight across multiple AI projects and guide teams toward shared capabilities
- Guide teams on responsible AI best practices
- Assess AI use-case suitability and recommend simpler approaches when appropriate
- Architect secure integrations with enterprise data, applications, APIs and workflow platforms
- Support retrieval-augmented generation, semantic search, prompt and context management, model evaluation, MLOps and LLMOps
- Ensure testing, monitoring, cost management, performance, resilience, auditability and lifecycle controls
- Contribute to AI governance and design assurance, present proposals to architecture and risk review forums, and track key decisions
- Evaluate emerging AI technologies and vendors through assessments and proofs of concept
- Collaborate with engineers and delivery teams to resolve design issues and maintain enterprise standards
- Build relationships across business and technology teams, facilitate workshops and identify measurable AI value
- Maintain awareness of AI technology, industry practice and regulation to evolve architecture guidance
- Take on additional duties appropriate to the role
- Report into the appropriate Architecture leadership function
Requirements
What you’ll need- At least 5 years of relevant experience in AI, machine learning, data science, software engineering, cloud architecture or solution architecture, including experience designing or delivering AI-enabled solutions
- Strong technical background with practical understanding of software engineering, cloud platforms, APIs, data integration, security and modern development practices
- Experience building/designing solutions across Databricks and Azure platforms
- Proven experience designing enterprise-grade AI architectures for security, scalability, resilience, performance, observability, integration, governance and cost control
- Working knowledge of generative AI and machine learning concepts, including large language models, embeddings, vector search, retrieval-augmented generation, agents, model evaluation and prompt design
- Experience designing solutions on a major cloud platform
- Understanding of MLOps or LLMOps, versioning, testing, deployment, monitoring, observability and lifecycle management
- Knowledge of responsible AI principles, including privacy, security, fairness, explainability, transparency, safety and human oversight
- Ability to evaluate AI use cases, expected value, data dependencies, constraints and operational impacts
- Experience creating architecture artefacts such as solution designs, integration views, data flows, decision records, standards and reusable patterns
- Previous experience with AI architecture governance, design assurance, standards, guardrails and architectural decision-making
- Experience translating common project needs into reusable components, shared services or enterprise patterns
- Ability to maintain a portfolio-level view, manage architectural dependencies and challenge duplication
- Good stakeholder management skills
- Ability to facilitate workshops, draw out requirements, challenge assumptions and build agreement
- Clear written and verbal communication, including explaining complex AI concepts to non-technical audiences
- Collaborative and pragmatic approach
- Curiosity, sound judgement and commitment to continuous learning
- Preferred: experience in insurance, reinsurance or regulated financial services
- Preferred: knowledge of Microsoft AI services such as Azure AI Foundry, Azure OpenAI, Copilot Studio or Microsoft Fabric
- Preferred: experience with Databricks, data platforms, semantic technologies, knowledge graphs or enterprise data governance
- Preferred: familiarity with AI risk-management frameworks, model governance, threat modelling and emerging AI regulation
- Preferred: experience supporting proofs of concept through production adoption
- Preferred: relevant cloud, architecture, data, security or AI certifications
Benefits
Comp & perks- Hybrid work model
- Diverse and inclusive environment
- Career growth opportunities
- Social impact initiatives
- Medical plans for you and your family
- Health and wellness programmes/programs
- Retirement plans
- Tuition reimbursement
- Paid annual leave
- Paid vacation
- Competitive target incentive compensation based on corporate and individual performance