{"id":3060902,"url":"https://alion.io/job/avanade-ai-delivery-lead","title":"AI Delivery Lead","company":{"id":712684,"name":"Avanade","domain":"avanade.com","url":"https://alion.io/company/avanade-3","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":106,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":13,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dubai, United Arab Emirates"],"countries":["AE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":131000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1199},"experience_years_min":11,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"BERT","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"OCR","optional":false},{"name":"OpenAI","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 Search","optional":false},{"name":"SharePoint","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Supabase","optional":false},{"name":"XGBoost","optional":false},{"name":"Azure Cosmos DB","optional":true},{"name":"FastAPI","optional":true},{"name":"Microsoft Teams","optional":true},{"name":"NER","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-22T06:31:19Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-23T03:59:49Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Role Overview\nWe are looking for an experienced Generative AI / Azure AI Engineering Lead with 11+ years of experience in IT, AI Engineering, Machine Learning, Generative AI, and cloud-based AI solution delivery. The candidate will be responsible for designing, developing, and deploying enterprise-grade AI solutions using Azure OpenAI, Microsoft Foundry / Azure AI Foundry, Azure AI Search, RAG, Agentic AI, Python, LangChain, Azure Functions, and cloud-native Azure services.\nThe role requires strong hands-on expertise in building production-ready Generative AI platforms involving retrieval-augmented generation, enterprise knowledge search, document intelligence, multi-agent workflows, structured AI outputs, evidence-based citations, workflow automation, and API-driven integrations.\nThe candidate should be capable of converting complex business requirements into scalable AI architectures, leading technical teams, defining solution blueprints, and delivering client-facing AI solutions for enterprise stakeholders.\nKey Responsibilities\nLead the architecture, design, development, and implementation of enterprise Generative AI solutions on Microsoft Azure.\n\nDesign and develop AI applications using Azure OpenAI, Microsoft Foundry / Azure AI Foundry, Azure AI Search, Azure Functions, Azure Blob Storage, Azure Content Understanding, and Azure Machine Learning.\n\nBuild end-to-end RAG-based applications using document ingestion, chunking, embeddings, vector search, hybrid search, semantic ranking, metadata filtering, and citation-grounded response generation.\n\nDesign and implement Agentic AI and multi-agent architectures using orchestrator agents, specialist agents, tool-based workflows, system prompts, structured prompts, and deterministic business logic.\n\nIntegrate Azure OpenAI models with enterprise applications for document summarization, knowledge retrieval, intelligent Q&A, business decision support, content generation, and workflow automation.\n\nDevelop Python-based orchestration pipelines for agent execution, business rule processing, scoring logic, evidence mapping, structured JSON response generation, exception handling, and API integration.\n\nImplement document intelligence workflows using OCR, text extraction, metadata enrichment, prebuilt/custom analyzers, content chunking, embedding generation, and vector indexing pipelines.\n\nConfigure and optimize Azure AI Search indexes for vector retrieval, keyword search, hybrid search, semantic search, filtering, ranking, and source traceability.\n\nDesign secure and scalable cloud-native AI solutions using Managed Identity, Azure AD / Entra ID, Azure Key Vault, RBAC, API security, logging, and monitoring.\n\nCollaborate with business stakeholders, product owners, architects, data engineers, application teams, and client teams to translate business requirements into scalable AI solution designs.\n\nDefine solution architecture, integration patterns, data flow, security model, deployment approach, and implementation roadmap for Generative AI solutions.\n\nEnsure AI responses are reliable, traceable, and business-ready through prompt engineering, grounding, hallucination control, citation validation, structured output design, and evaluation frameworks.\n\nLead technical teams across design, development, testing, debugging, deployment, production readiness, and support activities.\n\nTroubleshoot and optimize AI pipelines related to retrieval quality, prompt accuracy, model performance, token usage, vector indexing, search relevance, latency, and response reliability.\n\nPrepare technical documentation, architecture diagrams, implementation plans, proof-of-concepts, client-facing walkthroughs, and solution presentations.\n\n.\nRequired Skills\nGenerative AI & LLM Engineering\nStrong experience with Azure OpenAI, GPT models, LLM orchestration, prompt engineering, system prompts, function calling, structured output generation, and LLM-based application development. Hands-on experience in building RAG pipelines, Agentic AI workflows, multi-agent architectures, and enterprise chatbot solutions. Strong understanding of hallucination control, citation grounding, AI safety guardrails, model evaluation, response validation, and prompt optimization.\nAzure AI & Cloud Services\nStrong hands-on experience with Microsoft Foundry / Azure AI Foundry, Foundry Agents, Azure OpenAI Service, Azure AI Search, Azure Content Understanding, Azure Machine Learning, Azure Functions, Azure Blob Storage, Azure Key Vault, Azure Monitor, and Application Insights. Experience in designing cloud-native AI solutions with Managed Identity, Azure AD / Entra ID, RBAC, secure API access, logging, monitoring, and enterprise-grade access control.\nRAG & Enterprise Knowledge Search\nExperience in designing and implementing document ingestion pipelines, chunking strategies, embedding generation, vector indexing, vector search, hybrid search, semantic ranking, metadata filtering, and ACL-based retrieval. Strong understanding of SharePoint integration, enterprise content grounding, source traceability, document versioning, citation generation, and evidence-based AI responses.\nProgramming & Frameworks\nStrong programming experience in Python. Hands-on experience withFastAPI,LangChain,LlamaIndex,Promptflow, REST APIs, JSON schema design, SQL, PostgreSQL,Supabase, Git, CI/CD, Docker, and Azure Container Apps. Ability to design backend services, orchestration layers, reusable AI components, and API-driven integrations.\nMachine Learning & NLP\nStrong understanding of Machine Learning, NLP, text classification, namedentity recognition, semantic similarity, document classification, OCR pipelines, embeddings, and model deployment. Experience with ML/NLP techniques and models such as BERT, Word2Vec, TF-IDF, LSTM, RNN,XGBoost, supervised learning, andMLOpspractices.\nRequired Experience\n11+ years of overall IT experience in application development, AI engineering, machine learning, cloud solutions, or data-driven application delivery.\n\nHands-on experience in designing and deploying Generative AI solutions on Microsoft Azure.\n\nPractical experience with Azure OpenAI, Azure AI Search, RAG, prompt engineering, embeddings, vector databases, and enterprise knowledge search.\n\nExperience in building multi-agent AI solutions or agentic workflows using orchestrator andspecialist-agentpatterns.\n\nExperience in integrating AI solutions with enterprise applications, databases, APIs, SharePoint, cloud storage, and business workflows.\n\nExperience leading technical teams and working with business stakeholders, architects, product owners, and client teams.\n\nStrong experience in preparing architecture diagrams, technical design documents, implementation plans, and client-facing solution materials.\n\nPreferred Experience\nExperience in enterprise domains such as government, ports andlogistics, KYC, compliance, legal, finance, procurement, enterprise knowledge management, or customer service automation.\n\nExperience with document intelligence, video RAG, SharePoint-based knowledge retrieval, Microsoft Teams agents, and enterprise chatbot platforms.\n\nExperience in designing AI systems that require structured JSON outputs, scoring logic, decision support, risk assessment, evidence mapping, and source-level traceability.\n\nExperience with Snowflake, PostgreSQL,Supabase, Cosmos DB, Azure Blob Storage, and external API integrations.\n\nExperience with production-readiness activities including testing, monitoring, telemetry, logging, performance tuning, and AI response evaluation.\n\nTools & Technologies\nCloud & AI\nMicrosoft Foundry / Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Machine Learning, Azure Content Understanding, Azure Functions, Azure Blob Storage, Azure Key Vault, Azure Monitor, Application Insights\nFrameworks\nLangChain,LlamaIndex,Promptflow,FastAPI\nProgramming\nPython, SQL, REST APIs, JSON, Git\nData & Storage\nPostgreSQL,Supabase, Snowflake, Cosmos DB, Azure Blob Storage\nAI/ML\nRAG, LLMs, embeddings, vector search, NLP, BERT, LSTM,XGBoost, OCR, NER\nSecurity\nAzure AD / Entra ID, Managed Identity, RBAC, ACL-based retrieval, API security\nDevOps\nGit, CI/CD, Docker, Azure Container Apps, logging, monitoring, and deployment automation\nExpected Outcomes\nDeliver scalable, secure, and production-ready Generative AI solutions on Microsoft Azure.\n\nBuild reliable RAG and Agentic AI applications with strong grounding, citation traceability, and hallucination control.\n\nDesign enterprise-grade AI architectures that integrate with business systems, document repositories, databases, APIs, and cloud services.\n\nLead technical delivery from solution design to implementation, testing, deployment, and production support.\n\nSupport client-facing discussions, demos, technical walkthroughs, solution documentation, and delivery planning.\n\n.","description_format":"text","description_chars":8827,"description_truncated":false,"requirements":{"experience_years_min":11,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Professional Services"],"lifecycle":[{"event":"open","at":"2026-10-22T06:31:19Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely 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