{"id":2915182,"url":"https://alion.io/job/techcarrot-ai-engineer-2","title":"AI Engineer","company":{"id":89346,"name":"Techcarrot","domain":"techcarrot.ae","url":"https://alion.io/company/techcarrot","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":{"grade":"B","score":75,"open_postings":3,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Haystack","optional":false},{"name":"Hybrid Search","optional":false},{"name":"JavaScript","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"n8n","optional":false},{"name":"Neo4j","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Redis","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TypeScript","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-16T00:00:00Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-22T19:59:44Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"We are seeking a highly skilled and hands-on AI Engineer to design, build, and deliver enterprise-grade, production-ready AI, conversational AI, and agentic AI solutions that enhance how users interact with enterprise data, insights, recommendations, and business workflows.\nThis is primarily a hands-on Individual Contributor (IC) role, with approximately 90% focus on individual technical contribution and 10% on coordinating and reviewing tasks delivered by the offshore development team.\nThe role requires strong engineering capabilities across GenAI, AI/ML, data validation, backend development, and React application development, with end-to-end involvement from solution design and coding through testing, deployment, and production support.\nThe role goes beyond traditional chatbots. You will architect and deliver multi-agent, tool-augmented GenAI solutions capable of reasoning, planning, contextual retrieval, and action execution across multiple enterprise data sources and platforms. You will work on secure, scalable, and governed AI systems aligned with enterprise architecture and compliance standards, ensuring data quality, reliability, explainability, observability, and continuous improvement.\nGenAI & Agentic System Development\nDesign, develop, and deploy production-grade GenAI solutions using advanced LLMs such as OpenAI models.\nImplement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data.\nDesign hybridsearch architectures combining Vector DBs and Graph DBs (e.g., Azure AI\nSearch, Neo4j) for semantic, contextual, and relationship-based retrieval.\n\nBuild AgenticAI workflowsusing frameworks such as LangChain, LangGraph, and Haystack,\nincluding:\n\nMulti-agent orchestration (planner, retriever, evaluator, executor agents) \nTool-calling, function execution, and system-to-system automation\nMemory management (short-term, long-term, and session-based)\nData Validation & Quality\nPerform end-to-end data validation before data is consumed by AI, ML, analytics, and decision-intelligence applications.\nValidate data completeness, accuracy, consistency, freshness, aggregations, calculations, and business-rule alignment across source systems and downstream applications.\nWork with business and data teams to validate KPIs, calculations, business rules, AI-generated insights, and recommendations before production release.\nDevelop automated data-quality checks, validation frameworks, anomaly detection, and reconciliation processes.\nIdentify data-quality issues and coordinate with Data Engineering and relevant teams for resolution.\nEnsure AI-generated insights and recommendations are based on validated and trusted enterprise data.\nFull-Stack AI & React Application Development\nTake hands-on ownership of end-to-end AI application development, from AI/ML services and APIs through user-facing applications.\nDesign and develop modern, responsive React-based web applications for AI, analytics, and decision-intelligence use cases.\nBuild interactive interfaces for AI insights, recommendations, conversational experiences, dashboards, visualizations, and actionable workflows.\nIntegrate React applications with AI/ML services, enterprise APIs, data platforms, authentication services, and backend systems.\nDevelop scalable backend services and APIs using Python and relevant API frameworks.\nEnsure frontend and backend applications meet enterprise requirements for performance, security, scalability, usability, and maintainability.\nIndividual Contribution & Offshore Team Coordination\nSpend approximately 90% of the role as a hands-on Individual Contributor, directly involved in architecture, coding, development, debugging, testing, optimization, deployment, and production support.\nAllocate approximately 10% of the role to coordinating and reviewing technical tasks delivered by the offshore team.\nReview offshore team deliverables to ensure alignment with requirements, solution design, coding standards, and expected quality.\nPerform code reviews, technical reviews, and functional validation of assigned offshore deliverables.\nProvide clarification on technical requirements and tasks where required to support offshore delivery.\nTrack assigned technical tasks and highlight dependencies, quality issues, or delivery risks.\nWork collaboratively with offshore AI Engineers, Data Engineers, Data Scientists, and Frontend Developers on integrated solution delivery.\nRemain directly accountable for assigned hands-on development activities while supporting the quality and integration of offshore deliverables.\nEnterprise Integration & Cloud Engineering\nDevelopand integrate AI-powered chatbots and agents within the Azure ecosystem\n\nIntegrate applications with enterprise identity and access-management frameworks including authentication, authorization, RBAC, and data-level security\nBuildsecure, scalable backend leveraging Azure App Services, Azure Functions,\nBot Framework, Azure Cache for Redis, and related services.\n\nWorkclosely with Cloud, Digital, Data Engineering, and Business teams for end-to-end solution delivery\n\nProduction Readiness, MLOps & LLMOps\nImplement guardrails for safety, hallucination control, data privacy, and responsible AI\nMonitor production performance across availability, latency, accuracy, reliability, and scalability.\nEnsure enterprise-grade governance, including access control, auditability, and compliance with internal policies\nApply MLOps / LLMOps best practices across the lifecycle:\nModel/version management and prompt versioning\nCI/CD pipelines for GenAI applications\nAutomated testing (prompt, retrieval, and regression testing)\nMonitoring, logging, and observability for LLM outputs\nPerformance Optimization & Continuous Improvement\nAnalyze AI application and agent performance using metrics such as accuracy, response quality, latency, adoption, and task completion. \nOptimize prompts, retrieval strategies, agent flows, APIs, database queries, and application performance based on actual usage.\nIdentify and resolve performance bottlenecks across data, AI, backend, database, and frontend layers.\nDrive continuous improvement through experimentation, evaluation, monitoring, and user feedback.\nRequirements\nStrong hands-on understanding of LLMs, transformers, embeddings, prompt engineering, context engineering, RAG, and evaluation techniques. \nExperience building end-to-end GenAI and Agentic AI products from development through production deployment.\nHands-on experience with LangChain, LangGraph, Haystack, n8n, and Microsoft Copilot ecosystem or similar frameworks.\nPractical experience designing multi-agent architectures and orchestrating reasoning, retrieval, tools, and actions.\nStrong experience with Vector and Graph Databases, including Azure AI Search, Neo4j, and Databricks Vector Search.\nProven experience implementing RAG pipelines using structured and unstructured enterprise data.\nStrong experience with data validation, data-quality checks, reconciliation, KPI validation, and business-rule validation.\nStrong proficiency in Python, SQL, and Spark.\nHands-on experience developing React / JavaScript / TypeScript applications.\nExperience developing APIs and backend services using FastAPI, Flask, or equivalent frameworks.\nHands-on experience with PyTorch and/or TensorFlow.\nExperience working with large-scale AI/ML systems in production environments.\nExperience reviewing code and technical deliverables from distributed/offshore development teams.\nStrong problem-solving skills across AI, data, backend, frontend, and enterprise system integration.\nAzure & Data Platform Experience\nHands-on experience with relevant Azure and enterprise data technologies, including:Azure OpenAI \nAzure Data Factory (ADF)\nAzure Databricks\nAzure AI Search\nDatabricks Genie\nAzure AI Document Intelligence\nAzure App Services\nAzure Functions\nAzure Kubernetes Service (AKS)\nAzure Cache for Redis\nAzure Bot Service / Bot Framework\nAPI Management\nMicrosoft Entra ID / Azure AD\nCI/CD and DevOps tooling","description_format":"text","description_chars":8023,"description_truncated":false,"requirements":{"experience_years_min":null,"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":["IT Consulting & Digital Transformation","Custom Software Development","Enterprise Apps Implementation Partners"],"lifecycle":[{"event":"open","at":"2026-10-20T10:31:31Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.899,"p_room":1,"age_days":6,"expected_fill_days":26,"reasons":["conf:1","velocity","win:early"],"computed_at":"2026-10-22T06:10:10Z"},"pay":null,"html_url":"https://alion.io/job/techcarrot-ai-engineer-2","json_url":"https://alion.io/job/techcarrot-ai-engineer-2.json","meta":{"generated_at":"2026-10-22T23:04:55Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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