{"id":3979967,"url":"https://alion.io/job/ntt-data-romania-senior-generative-ai-ai-solution-architect","title":"Senior Generative AI / AI Solution Architect","company":{"id":4401319,"name":"NTT DATA Romania","domain":"nttdata.ro","url":"https://alion.io/company/nttdata-ro","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Timișoara, Romania"],"countries":["RO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"gRPC","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"IAM","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"TypeScript","optional":false},{"name":"Amazon ECS","optional":true},{"name":"Amazon EKS","optional":true},{"name":"AWS Bedrock AgentCore","optional":true},{"name":"AWS Glue","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Kubernetes","optional":true},{"name":"LiteLLM","optional":true},{"name":"Microsoft Fabric","optional":true}],"status":"live","first_seen_at":"2026-11-09T19:24:41Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-10T20:08:33Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Who We Are\nWe operate beyond organizational silos, combining deep expertise in data engineering and AI-driven solutions to design and deliver scalable, user-centric applications and modern data architectures. We support a wide range of industries, including Automotive, Banking, Insurance, Telecommunications, E-commerce, and Public Services.\nOur focus is on building robust, enterprise-grade data and AI platforms and enabling advanced analytics and AI capabilities across leading cloud ecosystems, including AWS and Microsoft Azure, as well as Microsoft Fabric and the Power Platform. We specialize in designing secure, scalable, high-performance solutions that integrate data platforms, AI models, and Generative AI use cases to deliver real business value.\nWhat you'll be doing\nDesign end-to-end architectures for Generative AI, agentic AI, enterprise knowledge solutions, document automation, and intelligent workflows.\nDefine agent architectures covering orchestration, tools, APIs, state and memory management, retrieval, model routing, human-in-the-loop patterns, observability, and failure recovery.\nDesign enterprise RAG solutions using semantic and hybrid search, vector and graph-based retrieval, metadata filtering, access controls, citations, reranking, evaluation, and feedback loops.\nArchitect foundation-model integration and model gateway patterns, including model selection, routing, fallback strategies, prompt and context management, safety controls, latency, and cost-performance optimization.\nDevelop document intelligence and structured-generation patterns for proposals, reports, validation workflows, and engineering artefacts.\nDesign scalable serverless, containerized, API-based, and event-driven solutions across AWS, hybrid, and on-premises environments.\nDefine security, identity, networking, observability, evaluation, deployment, governance, and LLMOps standards for production AI solutions.\nBuild prototypes and reference implementations to validate architectural approaches and emerging technologies.\nReview solution designs and code, resolve critical technical challenges, and provide technical guidance to engineering teams.\nMentor engineers and contribute to architecture standards, best practices, and technology decisions across projects.\nWhat you'll bring along\nAt least 8 years of relevant experience across software engineering, cloud, data, platform engineering, or AI, including significant experience designing enterprise-scale solutions.\nProven experience designing and delivering production AI and machine-learning solutions.\nStrong hands-on experience designing and delivering production Generative AI and LLM solutions, including RAG, embeddings, retrieval, evaluation, guardrails, and agentic workflows.\nStrong software engineering background, preferably with hands-on Python experience and familiarity with one or more additional languages such as Java, C#, TypeScript/JavaScript, Go, or equivalent.\nStrong understanding of REST, gRPC, asynchronous processing, event-driven architectures, and enterprise integration patterns.\nProven architecture experience across public cloud and hybrid, private-cloud, or on-premises environments.\nHands-on experience with AWS AI and application services, including Amazon Bedrock or comparable foundation-model platforms.\nExperience designing retrieval architectures using technologies such as Amazon OpenSearch, PostgreSQL/pgvector, vector databases, and/or graph-based retrieval systems.\nStrong knowledge of IAM, private networking, secrets management, observability, CI/CD, infrastructure as code, AI evaluation, safety, and production LLMOps.\nStrong understanding of production AI considerations such as scalability, reliability, latency, security, governance, and cost optimization.\nStrong client-facing consulting and technical communication skills, with the ability to translate complex business requirements into scalable technical solutions.\nA high degree of ownership, collaboration, technical decision-making, and mentoring capability.\nExcellent command of both spoken and written English.\nNice to have\nExperience with Amazon Bedrock AgentCore, Bedrock Guardrails, AWS Glue, Lambda, ECS, or EKS.\nExperience with LiteLLM, open-source models, model gateways, or multi-model architectures.\nExperience with knowledge graphs and graph-based retrieval.\nExperience with Azure AI or other enterprise AI platforms.\nExperience designing AI solutions for regulated or security-sensitive environments.\nRelevant AWS, Azure, AI, cloud, or 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