{"id":178900,"url":"https://alion.io/job/ssc-hr-solutions-senior-ai-engineer","title":"Senior AI Engineer - Generative AI & Azure AI Platform","company":{"id":3460,"name":"SSC HR Solutions","domain":"sschrsolutions.com","url":"https://alion.io/company/sschrsolutions","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":95,"open_postings":13,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-26T05:57:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New Cairo, Egypt"],"countries":["EG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":14500,"max_usd":37000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2365},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google AI Studio","optional":false},{"name":"gRPC","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Entra ID","optional":false},{"name":"Mistral","optional":false},{"name":"ONNX Runtime","optional":false},{"name":"OpenAI","optional":false},{"name":"OWASP Top 10","optional":false},{"name":"PEFT","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Threat Modeling","optional":false},{"name":"Tokenization","optional":false},{"name":"Vertex AI","optional":false},{"name":"vLLM","optional":false},{"name":"AI Agents","optional":true},{"name":"AutoGen","optional":true},{"name":"Computer Vision","optional":true},{"name":"EU AI Act","optional":true},{"name":"Function Calling","optional":true},{"name":"GDPR","optional":true},{"name":"Langfuse","optional":true},{"name":"MLFlow","optional":true},{"name":"Multi-Agent Systems","optional":true},{"name":"NIST AI RMF","optional":true},{"name":"NLP","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Transformers","optional":true},{"name":"Weaviate","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-08-30T00:00:00Z","employer_posted_date":"2026-08-30","last_verified_at":"2026-10-26T14:11:42Z","board_verified":true,"closed_at":null,"days_open":57,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":57},"description":"About the Role\nWe are looking for a Senior AI Engineer to design, build, and operate production-grade Generative AI solutions on the Microsoft Azure AI ecosystem. You will be the technical anchor for our GenAI initiatives, owning the end-to-end lifecycle: from foundation model selection and prompt/RAG architecture through deployment, MLOps, security hardening, and continuous evaluation. This is a hands-on senior role. You will set technical direction, mentor engineers, and work directly with product, data, security, and platform teams to move AI use cases from prototype to reliable, governed, cost-efficient services.\nWhat You Will do\nGenAI solution design and delivery\nArchitect and build LLM-powered applications (RAG, agents, copilots, document intelligence, content understanding, conversational systems) using Azure AI Foundry, Azure OpenAI, and the broader Azure AI and data portfolio\n.\nDesign retrieval pipelines with Azure AI Search (vector, hybrid, and semantic ranking), including chunking, embedding, indexing, and relevance tuning strategies.\nEvaluate, fine-tune, and deploy foundation and open-source models (e.g., GPT, Phi, Llama, Mistral, Kimi, GLM) through Azure AI Foundry model catalog and Azure Machine Learning.\nImplement prompt engineering, orchestration frameworks (Semantic Kernel, LangChain, Prompt Flow, or equivalent), and structured evaluation of model quality, groundedness, and safety.\nPlatform, MLOps, and productionization\nBuild and maintain MLOps/LLMOps pipelines on Azure ML, Github Enterprise: experiment tracking, model registry, CI/CD for models and prompts, automated evaluation, monitoring, and drift/cost management.\nExpose AI capabilities as scalable microservices (Azure Kubernetes Service, Azure Container Apps, Azure Functions, API Management), with attention to latency, throughput, resilience, and cost.\nEstablish observability for AI systems: tracing, token/cost telemetry, quality metrics, and feedback loops.\nAI security and governance\nApply Responsible AI and AI security practices: content safety filters, prompt-injection and jailbreak mitigation, data-leakage controls, PII handling, and red-teaming.\nImplement secure architectures using Azure identity (Entra ID, managed identities), private endpoints, Key Vault, network isolation, and data residency controls.\nContribute to AI governance standards, model risk documentation, and compliance requirements.\nTechnical leadership\nDefine reference architectures, coding standards, and reusable components for GenAI workloads.\nMentor and review the work of other engineers; lead design discussions and technical decision-making.\nPartner with stakeholders to translate business problems into feasible, measurable AI solutions and communicate trade-offs clearly.\nStay current with the rapidly evolving model and tooling landscape and bring practical recommendations to the team.\nRequirements\nRequired Qualifications\n8-10+ years of overall experience in AI, machine learning, and data engineering, with a strong track record of delivering production systems.\nMinimum 2-3 years of hands-on experience with Azure AI Foundry (formerly Azure AI Studio) and Azure OpenAI Service, including deploying and operating GenAI applications in production.\nDeep expertise across the Azure AI ecosystem: Azure Machine Learning, Azure AI Search, Azure AI Services (Document Intelligence, Language, Speech, Vision), Fabric and Azure AI Content Safety.\nStrong understanding of LLMs and foundation models: architectures, tokenization, context management, embeddings, fine-tuning (LoRA/PEFT), quantization, and evaluation methods.\nPractical experience with open-source models and frameworks (Hugging Face, Llama, Mistral, Phi, vLLM/ONNX Runtime or similar serving stacks).\nProven MLOps/LLMOps experience: CI/CD, model versioning, automated testing and evaluation, monitoring, and rollback strategies.\nExperience designing and building microservices and APIs (containers, Kubernetes, REST/gRPC, event-driven patterns) with Azure DevOps or GitHub Actions.\nDemonstrated knowledge of AI security and Responsible AI: threat modeling for LLM applications (OWASP Top 10 for LLMs), data protection, access control, and safety guardrails.\nWorking experience with at least one other cloud provider (AWS or GCP) and their AI/ML services (e.g., Amazon Bedrock, SageMaker, Vertex AI).\nExpert-level Python; solid software engineering fundamentals (testing, code review, design patterns, performance optimization).\nStrong data foundations: SQL, data pipelines, data modeling, and familiarity with Azure data services (Data Factory, Databricks, Synapse, Fabric, Cosmos DB, or equivalent).\nExcellent communication skills with the ability to explain complex AI concepts to technical and non-technical audiences.\nPreferred Qualifications\nMicrosoft certifications: AI-102 (Azure AI Engineer Associate), DP-100 (Azure Data Scientist Associate), or AZ-305.\nExperience with agentic AI patterns, multi-agent orchestration, and tool/function calling (Azure AI Agent Service, Semantic Kernel agents, AutoGen, or similar).\nExperience with vector databases beyond Azure AI Search (e.g., Cosmos DB vector search, PostgreSQL pgvector, Pinecone, Weaviate).\nBackground in NLP, computer vision, or speech systems prior to the GenAI era.\nExperience with model evaluation and observability tooling (Azure AI evaluation SDK, Prompt Flow evaluations, Langfuse, MLflow, Weights & Biases).\nFamiliarity with regulatory and compliance frameworks relevant to AI (GDPR, ISO/IEC 42001, NIST AI RMF, EU AI Act, or regional data protection regulations).\nExperience operating in regulated industries (financial services, healthcare, government) or large-enterprise environments.\nContributions to open-source projects, publications, or technical community engagemen\nKey Competencies\nOwnership and delivery focus in ambiguous, fast-moving environments\nSystems thinking: balancing model quality, latency, cost, security, and maintainability\nPragmatism about when GenAI is (and is not) the right tool\nMentorship and collaborative technical leadership\nWhat We Offer\nCompetitive market compensation and benefits.\nOpportunity to shape the AI platform and standards for a fast growing startup in UAE.\nAccess to Azure and partner resources, certifications, and continuous learning budget 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