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AI Architect
Titan Decorative Materials Group. Act as the global lead and subject matter expert for AI-related initiatives, including GenAI, across TITAN Group .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates deep expertise in AI and LLM architecture patterns, including AI engineering, orchestration, and governance. Proficient in designing scalable AI solutions and integrating them with enterprise applications while ensuring compliance with security and regulatory standards.
Highest-signal resume keywords
AI Architecture PatternsLangChain ExperienceAI Governance ExpertisePython ProgrammingCloud-Platform AI Ecosystems
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 EngineeringLLM OrchestrationRAG ArchitecturesPrompt EngineeringModel Lifecycle ManagementData Pipeline DesignEmbedding StrategiesContainerizationIntegration TechnologiesVersioning and Drift Detection
Soft Skills
Excellent Communication SkillsMentoring and LeadershipCommunity Building
Tools & Technologies
LangGraphLlamaIndexSemantic KernelAzureAPIsMCPsVector DatabasesEvent-Driven Systems
Industry Keywords
AI Governance FrameworksData PrivacyModel Risk ManagementEnterprise Security StandardsRegulatory Compliance
Tech Stack
Tools & technologiesAzureCloudPythonSQL
About the role
Key responsibilities & impact- Act as the global lead and subject matter expert for AI-related initiatives, including GenAI, across TITAN Group
- Design the overall AI orchestration architecture and blueprints for AI platforms and their integration with the Group’s IT and digital technology stack
- Design AI pipelines, including RAG architectures, LLM orchestration layers, agent workflows, and evaluation frameworks
- Translate business and regulatory requirements into secure, scalable, production-grade technical blueprints
- Define reference architectures, reusable patterns, and engineering standards for AI adoption at scale
- Evaluate vendors and platforms
- Write production-quality Python and related frameworks
- Integrate and validate MVPs, PoCs, and acquired AI solutions
- Implement agentic AI systems, including tool use, multi-agent orchestration, MCPs, memory management, and autonomous decision workflows
- Deploy and integrate AI components into enterprise applications, APIs, data platforms, and third-party systems
- Design observability stacks for prompt monitoring, model response tracking, vector retrieval quality, and agent execution workflows
- Manage model lifecycles, including versioning, drift detection, evaluation-driven development, retraining pipelines, bias, fairness, and transparency
- Optimize inference cost, latency, and throughput at scale
- Establish and enforce AI governance frameworks covering auditability, data privacy, and model risk management
- Ensure compliance with enterprise security standards, including IAM, encryption, and regulatory requirements
- Develop tools and methodologies to monitor and optimize AI usage costs in collaboration with FinOps
- Drive responsible AI practices across multi-team and multi-vendor environments
- Advise senior leadership and business units on technical matters
- Mentor engineers and architects and foster engineering excellence
- Lead knowledge-sharing, community-building, and internal AI enablement initiatives
- Stay current with the AI landscape and introduce tools, frameworks, and patterns to improve team capability
Requirements
What you’ll need- Deep expertise in AI and LLM architecture patterns, including AI engineering and orchestration, RAG, fine-tuning, prompt engineering, and multi-model strategies
- Hands-on experience with LangChain, LangGraph, LlamaIndex, and Semantic Kernel
- Experience with foundation model evaluation, vector databases, and embedding strategies
- Knowledge of open-source and commercial LLM ecosystems
- Knowledge of APIs, MCPs, integration technologies, and protocols
- Knowledge of containerization and orchestration tools
- Familiarity with AI coding languages, ML and GenAI/LLM frameworks, cloud-platform AI ecosystems, and toolkits, especially Azure
- Knowledge of AI impacts on data architecture, including SQL, vector databases, data pipelines, APIs, and event-driven systems
- Expertise in AI and GenAI agent governance, including IAM, encryption, enterprise governance, and compliance controls
- Bachelor’s degree in a relevant field such as computer science, AI engineering, or another STEM-related field
- Minimum of 5 years’ experience in a relevant position
- Experience implementing and operating AI architectures and orchestration layers in complex environments
- Experience managing complex strategic initiatives, including in multinational environments
- Excellent written and verbal communication skills in English
- Willingness to travel internationally as required
- Military obligations fulfilled, where applicable
Benefits
Comp & perks- Hybrid workplace arrangement
- Opportunities for coaching, knowledge sharing, and capability building
- International travel opportunities across TITAN Group’s business units and operations