{"id":1652873,"url":"https://alion.io/job/codeninja-senior-ai-engineer-agentic-ai-architect","title":"Senior AI Engineer / Agentic AI Architect","company":{"id":5276,"name":"CodeNinja","domain":"codeninja.com","url":"https://alion.io/company/codeninja","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":90,"open_postings":9,"ghost_share":0,"stale_share":0.333,"repost_share":0.111,"time_to_fill_p50_days":27,"computed_at":"2026-10-20T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"contractor","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Riyadh, Saudi Arabia","Lahore, 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Biases","optional":false},{"name":"Anthropic","optional":true},{"name":"Databricks","optional":true},{"name":"LangChain","optional":true},{"name":"OCR","optional":true},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-10-01T00:00:00Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-20T05:00:11Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"Description\nAbout CodeNinja\nCodeNinja is a global software and AI infrastructure company delivering full-stack technology solutions across AI, software engineering, data, and digital transformation.\nWith operations across Saudi Arabia and global technology hubs, CodeNinja works with organizations across multiple industries to deliver technology solutions that support business transformation and innovation.\nOur teams work across areas including AI, software engineering, data and analytics, cloud, enterprise technology, and digital transformation.\nCodeNinja is looking for an experienced Senior AI Engineer / Agentic AI Architect to design, build, and deploy enterprise-scale AI solutions for the banking and financial services sector.\nAbout the Role\nIn this role, you will deliver production-grade AI applications with a focus on scalability, security, observability, governance, and performance. You will work across Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI.\nThe ideal candidate will have strong software engineering fundamentals and deep expertise in LLMs, RAG, Agentic AI, and modern AI engineering practices. They will have 8-12+ years in software engineering, including 5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.\nKey Responsibilities\nDesign and develop enterprise-grade AI solutions using modern LLMs and Agentic AI frameworks.\nArchitect multi-agent systems capable of planning, reasoning, tool usage, and workflow orchestration.\nBuild production-ready RAG platforms integrating structured and unstructured enterprise data.\nDesign scalable APIs and AI services using Python and modern backend frameworks.\nImplement robust evaluation frameworks for LLM quality, safety, and performance.\nOptimize AI applications for latency, throughput, and infrastructure cost.\nDeploy and manage open-source LLMs in production environments.\nCollaborate with architects, product owners, business analysts, and DevOps teams to deliver enterprise AI platforms.\nEnsure compliance with enterprise security, governance, and responsible AI practices.\nMentor engineering teams and contribute to AI best practices and reusable frameworks.\nExpected Deliverables\nThe selected candidate should be capable of independently designing and delivering:\nEnterprise AI platforms\nMulti-agent AI systems\nRAG-based knowledge assistants\nAI copilots\nLLM evaluation frameworks\nProduction-ready AI APIs\nAI observability and monitoring solutions\nSecure, scalable, and cost-optimized AI deployments suitable for enterprise production environments.\nRequirements\nRequirements\nRequired Qualifications & Skills\nExperience\n8-12+ years of experience in Software Engineering.\n5+ years of hands-on experience in Artificial Intelligence, Machine Learning, and Generative AI.\nAI / Machine Learning\nStrong understanding of: \nMachine Learning\nDeep Learning\nNLP\nTransformer architectures\nLarge Language Models (LLMs)\nEmbedding models\nSoftware Engineering\nExpert-level Python programming.\nStrong software engineering fundamentals.\nExperience building production-grade backend systems.\nRESTful API and microservices development.\nAsync programming and scalable architectures.\nExperience with FastAPI, Flask, or similar frameworks.\nAgentic AI\nHands-on experience designing and implementing Agentic AI solutions using one or more of: \nLangGraph\nCrewAI\nOpenAI Agents SDK\nAutoGen\nSemantic Kernel\nLlamaIndex Workflows\nExperience in: \nMulti-agent orchestration\nPlanning agents\nTool calling\nHuman-in-the-loop workflows\nMemory management\nState management\nAgent collaboration patterns\nRetrieval-Augmented Generation (RAG)\nStrong experience building enterprise RAG platforms.\nEmbedding models, such as: \nOpenAI\nVoyage AI\nBGE\nE5\nInstructor\nCohere\nVector databases, such as: \nPinecone\nQdrant\nMilvus\nWeaviate\nChromaDB\nGraph databases, such as: \nNeo4j\nAmazon Neptune\nMemgraph\nSearch technologies, including: \nHybrid Search\nBM25\nDense Retrieval\nSparse Retrieval\nSemantic Search\nMetadata Filtering\nRe-ranking\nKnowledge Graph integration\nLLM Evaluation\nExperience designing systematic evaluation frameworks using tools such as: \nRagas\nTruLens\nDeepEval\nOpenAI Evals\nLangSmith Evaluation\nUnderstanding of: \nHallucination detection\nFaithfulness\nAnswer relevancy\nContext precision\nContext recall\nGroundedness\nToxicity\nRegression testing\nGuardrails & Observability\nGuardrails: \nGuardrails AI\nNeMo Guardrails\nOpenAI Moderation\nPrompt Injection Detection\nPII masking\nContent filtering\nObservability: \nLangSmith\nLangfuse\nArize Phoenix\nWeights & Biases\nMLflow\nExperience with: \nPrompt tracing\nToken analytics\nCost monitoring\nLatency monitoring\nUser feedback loops\nProduction debugging\nPrompt Engineering\nExpertise in: \nChain-of-Thought (CoT)\nReAct\nTree of Thoughts\nSelf-Consistency\nFew-shot prompting\nStructured prompting\nFunction Calling\nJSON mode\nPrompt optimization\nPrompt caching\nContext window optimization\nToken usage optimization\nCost optimization\nModel Deployment & Inference\nHands-on experience deploying open-source LLMs.\nPreferred models: \nLlama\nMistral\nQwen\nGemma\nDeepSeek\nInference engines: \nvLLM\nTensorRT-LLM\nOllama\nTGI (Text Generation Inference)\nSGLang\nExperience with: \nGPU optimization\nBatch inference\nModel serving\nAutoscaling\nMulti-GPU deployment\nQuantization (GGUF, GPTQ, AWQ, FP8, INT8, INT4)\nMLOps / AI Platform\nExperience with: \nMLflow\nKubeflow\nDocker\nKubernetes\nGitHub Actions / GitLab CI\nModel versioning\nExperiment tracking\nFeature stores\nContinuous evaluation\nContinuous deployment\nCloud Platforms\nExperience with one or more: \nGoogle Cloud Platform (Vertex AI)\nMicrosoft Azure AI\nAWS Bedrock\nOpenAI Azure\nDatabase Technologies\nExperience with: \nPostgreSQL\nOracle\nMongoDB\nRedis\nElasticsearch / OpenSearch\nSoft Skills\nStrong analytical and problem-solving skills.\nExcellent communication and stakeholder management.\nAbility to lead technical discussions and architecture reviews.\nExperience mentoring engineering teams.\nAbility to work in Agile delivery environments.\nBanking & Industry Experience (Nice to Have)\nBanking or Financial Services domain experience.\nExperience with enterprise AI governance and Responsible AI frameworks.\nKnowledge of SAMA, NCA, or other financial regulatory environments.\nExperience building AI copilots and enterprise AI assistants.\nKnowledge of OCR, document intelligence, and intelligent automation.\nExperience integrating AI solutions with BPM/workflow platforms such as Appian, Camunda, or Pega.\nEducation & Certifications\nBachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field.\nThe following professional certifications are an advantage: \nGoogle Professional Machine Learning Engineer\nMicrosoft Azure AI Engineer Associate\nAWS Certified Machine Learning - Specialty\nDatabricks Machine Learning Professional\nNVIDIA AI Certifications\nOpenAI or Anthropic ecosystem certifications (where applicable)\nKey Competencies\nAgentic AI | Multi-Agent Systems | Large Language Models (LLMs) | RAG | Vector & Graph Databases | LLM Evaluation | Guardrails & Observability | Prompt Engineering | LLM Deployment & Inference | MLOps | Python | APIs & Microservices | Cloud AI Platforms | Responsible AI\nBenefits\nBenefits\nWhat We Offer\nCompetitive compensation based on experience and qualifications.\nOpportunity to work on enterprise-scale technology and digital transformation projects.\nExposure to banking, financial services, AI, data, and emerging technology environments.\nProfessional growth and learning opportunities.\nCollaborative and technically driven work environment.\nOpportunity to work with experienced technology and consulting professionals.\nDisclaimer\nThis job description is intended to convey information essential to understanding the scope of the role and is not exhaustive of all responsibilities, skills, or qualifications required. 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