{"id":2445747,"url":"https://alion.io/job/tawantech-senior-ai-solution-architect","title":"Senior AI Solution Architect","company":{"id":6536,"name":"Tawantech","domain":"tawantech.com","url":"https://alion.io/company/tawantech","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":92,"open_postings":6,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":55,"computed_at":"2026-10-14T06:34:16Z"}},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Riyadh, Saudi Arabia"],"countries":["SA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":126000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1388},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"TensorFlow","optional":false},{"name":"AI Agents","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Docker","optional":true},{"name":"Function Calling","optional":true},{"name":"Hallucination","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Milvus","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Tool Use","optional":true},{"name":"Vertex AI","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-13T13:28:45Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T21:03:21Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Summary\nWe are seeking an experienced AI Solution Architect with 8-10 years of experience in Artificial Intelligence (AI), Machine Learning (ML), and enterprise solution architecture. The ideal candidate will be responsible for designing, developing, and implementing scalable, secure, and high-performance AI solutions aligned with business objectives.\nThe candidate should have strong expertise in AI/ML architecture, Generative AI, Large Language Models (LLMs), cloud-based AI platforms, and enterprise system integration. The role requires close collaboration with business stakeholders, data scientists, software engineers, and technology teams to translate business requirements into effective AI-driven solutions.\nKey Responsibilities\nDesign end-to-end AI/ML solution architectures aligned with business requirements and enterprise technology standards.\nDefine AI strategy, architecture patterns, technology stacks, and implementation roadmaps.\nArchitect and implement AI, ML, Generative AI, and LLM-based solutions for enterprise use cases.\nDesign Retrieval-Augmented Generation (RAG) architectures, vector database integrations, and knowledge retrieval pipelines.\nEvaluate and select appropriate AI models, frameworks, platforms, and cloud services based on business and technical requirements.\nDevelop architecture for scalable AI applications, APIs, microservices, and enterprise integrations.\nDesign and implement AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.\nEstablish best practices for model deployment, monitoring, versioning, performance optimization, and lifecycle management using MLOps principles.\nDefine responsible AI practices, including data privacy, security, explainability, bias mitigation, and governance.\nAssess the feasibility, scalability, cost, and business value of proposed AI solutions.\nCollaborate with data scientists, ML engineers, data engineers, developers, and business stakeholders throughout the solution lifecycle.\nDevelop architecture diagrams, technical specifications, proof of concepts (POCs), and implementation plans.\nGuide development teams on AI architecture, coding standards, integration patterns, and deployment practices.\nMonitor emerging AI technologies and recommend innovations that improve business outcomes.\nEnsure AI solutions meet enterprise security, compliance, performance, availability, and reliability requirements.\nRequired Skills and Qualifications\n8-10 years of experience in AI, Machine Learning, data science, software engineering, or AI-focused solution architecture.\nProven experience designing and delivering enterprise-scale AI/ML solutions.\nStrong understanding of Machine Learning, Deep Learning, Natural Language Processing (NLP), and predictive analytics.\nHands-on experience with Generative AI, LLMs, prompt engineering, and LLM application development.\nExperience designing RAG pipelines, embeddings, vector databases, and semantic search solutions.\nProficiency in Python and familiarity with AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.\nExperience with AI frameworks and orchestration tools such as LangChain, LlamaIndex, or equivalent technologies.\nStrong knowledge of REST APIs, microservices, distributed systems, and enterprise integration patterns.\nExperience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud.\nUnderstanding of data engineering concepts, data pipelines, data lakes, data warehouses, and data governance.\nKnowledge of MLOps practices, CI/CD pipelines, model monitoring, and production deployment.\nStrong understanding of AI security, data protection, responsible AI, and governance frameworks.\nExcellent architectural design, analytical, problem-solving, communication, and stakeholder management skills.\nPreferred Qualifications\nExperience with enterprise AI platforms such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent services.\nExperience implementing AI agents, tool calling, multi-agent workflows, and enterprise copilots.\nFamiliarity with vector databases such as Pinecone, Weaviate, Milvus, or pgvector.\nExperience with containerization and orchestration technologies such as Docker and Kubernetes.\nKnowledge of model evaluation, hallucination mitigation, guardrails, and AI observability.\nExperience designing hybrid or multi-cloud AI architectures.\nRelevant certifications in cloud architecture, AI/ML, or Generative AI.\nExperience leading technical teams and presenting architecture solutions to senior stakeholders.\nKey Competencies\nAI/ML solution architecture and technology strategy.\nGenerative AI, LLMs, and RAG implementation.\nEnterprise architecture and system integration.\nCloud-native AI solution design.\nMLOps and production AI lifecycle management.\nAI security, governance, and responsible AI.\nTechnical leadership and stakeholder collaboration.\nPerformance, scalability, and cost optimization.","description_format":"text","description_chars":4941,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Financial Software & Embedded Finance","Payment Processing & 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