{"id":191850,"url":"https://alion.io/job/aptara-ai-architect","title":"AI Architect","company":{"id":96696,"name":"Aptara","domain":"aptaracorp.com","url":"https://alion.io/company/aptara","size_band":"1-10","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Django","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"IAM","optional":false},{"name":"ISO 27001","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Weaviate","optional":false},{"name":"WebSockets","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-09-02T17:36:07Z","employer_posted_date":null,"last_verified_at":"2026-09-02T17:36:07Z","board_verified":false,"closed_at":null,"days_open":45,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":45},"description":"The role has the objective of harnessing AI and automation technologies to optimize and elevate eLearning development processes by creating scalable and efficient AI solutions. The role aims to enhance productivity, improve accuracy, and accelerate content creation in the eLearning development lifecycle. It also involves enhancing the learning experience by implementing AI techniques in learning modalities and learning platforms. The focus is on driving innovation to streamline workflows, reduce manual effort and thus bugs/errors, and deliver high-quality, engaging learning experiences.\nResponsibilities:\nLead AI strategy and define end-to-end architecture for eLearning and enterprise AI solutions, aligning with business objectives and transformation goals\nCollaborate with business, product, and technology stakeholders to identify use cases and translate requirements into scalable, enterprise-grade architectures\nOwn overall system architecture across application, data, and AI layers, ensuring reusability, extensibility, and alignment with engineering standards\nDesign secure AI systems leveraging LLMs (e. g., GPT models), including RAG pipelines and agentic workflows using frameworks such as LangChain or Semantic Kernel\nDefine AI orchestration patterns covering prompt design, context management, multi-turn interactions, and tool/agent coordination\nArchitect AI-driven capabilities, including automated content generation and multimodal experiences (text, audio, video, and image)\nEstablish data architecture and governance frameworks, including data sourcing, quality, compliance, and scalable pipelines for structured and unstructured data\nDefine cloud architecture and deployment strategy across Microsoft Azure, Amazon Web Services, or Google Cloud Platform, ensuring secure networking (VPC/VNet), private access, and scalable infrastructure\nEnsure solutions are secure, reliable, and cost-efficient by applying best practices in performance optimization, monitoring, and scaling\nEstablish and enforce security, governance, and Responsible AI practices, ensuring compliance with standards such as GDPR, ISO 27001 and ISO/IEC 42001 and mitigating risks like prompt injection and data leakage\nDefine and oversee MLOps/DevOps practices, including CI/CD, containerization, model lifecycle management, monitoring, and logging\nArchitect integration strategies to embed AI capabilities into enterprise platforms (LMS/LXPSCORM, xAPI, LTI, CMS, CRM) via APIs and microservices\nEstablish coding standards and API design guidelines, and conduct architecture and code reviews to ensure quality and consistency\nCollaborate with cross-functional teams across AI, engineering, product, and business to drive successful delivery\nRequirements:\nCandidates should have 10+ years of experience in web application development using DevOps. 5+ years in AI system design with exposure to cloud-native architectures.\nBachelor's/Master's in Computer Science, Engineering, or Data Science, or equivalent practical experience.\n5+ years in AI system design with exposure to cloud-native architectures.\nHands-on experience with LLMs (OpenAI, open-source models, etc. ), RAG pipelines and vector databases, and API-based AI integration.\nExperience with at least one major cloud platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform.\nUnderstanding of secure AI architecture (data isolation, private endpoints, IAM).\nKey Skills:\nProven experience designing and deploying ML and generative AI solutions into production with operational support.\nStrong expertise in LLMs (e. g., GPT models, LLaMA), including prompt engineering, evaluation, and optimization.\nHands-on experience building agentic AI systems and RAG pipelines and working with vector databases using frameworks like LangChain or Semantic Kernel.\nStrong programming skills in Python with experience in designing and integrating RESTful APIs.\nExperience with cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform, including secure and scalable deployments.\nSolid understanding of data pipelines, data governance, and AI system architecture.\nExperience with MLOps practices, including model lifecycle management, CI/CD, monitoring, and performance optimization.\nStrong understanding of AI security and Responsible AI principles, including data privacy, compliance, and risk mitigation.\nAI: OpenAI API / Anthropic Claude API, prompt engineering, RAG, LangChain or similar orchestration framework, embeddings, and vector databases (Pinecone / Weaviate / pgvector).\nBackend: Python (FastAPI / Django) or Node.js, REST APIs, WebSockets for real-time chat, session/state management (AWS or Azure (hands-on, not just theoretical)), VPC, IAM, EC2/ECS/EKS, S3 RDS.\nDevOps: Docker, Kubernetes, CI/CD pipelines, GitHub Actions, or equivalent.\nSecurity: IAM design, secrets management, encryption, and basic AppSec awareness.\nDatabase: PostgreSQL or equivalent relational DB, plus a vector store for 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