411_2965502<\/p>\n
Stellar Technologies is seeking a Machine Learning Engineer (GenAI) <\/b> to design, build, and deploy next\u2011generation AI systems combining Large Language Models (LLMs) <\/b>, Retrieval\u2011Augmented Generation (RAG) <\/b>, \u0648 agentic AI frameworks <\/b>. <\/p>\n
In this role, you will bridge model development and production engineering \u2014 developing scalable AI pipelines, integrating real\u2011time APIs, and ensuring high\u2011performance AI services that power enterprise\u2011grade solutions. You will work at the intersection of machine learning, cloud infrastructure, and applied research, collaborating with top engineers and data scientists to deliver intelligent, production\u2011ready AI capabilities. <\/p>\n
Develop and optimize AI systems leveraging LLMs, RAG, and agentic AI frameworks <\/b> (LangChain, LangGraph). <\/p>\n<\/li>\n Build and deploy production\u2011grade ML pipelines <\/b> with real\u2011time inference and retrieval components. <\/p>\n<\/li>\n Design and manage APIs and streaming services to integrate AI models into enterprise platforms. <\/p>\n<\/li>\n Implement containerized, orchestrated deployments using Docker, Kubernetes, and Azure ML <\/b>. <\/p>\n<\/li>\n Automate data preprocessing, model training, evaluation, and versioning pipelines. <\/p>\n<\/li>\n Collaborate with cross\u2011functional teams to integrate models into front\u2011end, analytics, and automation workflows. <\/p>\n<\/li>\n Ensure governance, compliance, and security of deployed AI workloads. <\/p>\n<\/li>\n Conduct performance benchmarking and optimize inference latency and cost. <\/p>\n<\/li>\n Monitor AI systems in production using observability frameworks (logging, metrics, tracing). <\/p>\n<\/li>\n Participate in architecture discussions to enhance scalability and reliability of AI services. <\/p>\n<\/li>\n<\/ul>\n Strong hands\u2011on experience with LLMs, RAG, and agentic frameworks <\/b> (LangChain, LangGraph, Semantic Kernel, etc.). <\/p>\n<\/li>\n Proficiency in Python <\/b>, with deep understanding of ML libraries like PyTorch, TensorFlow, scikit\u2011learn, Hugging Face Transformers <\/b>. <\/p>\n<\/li>\n Solid experience in API and microservices engineering <\/b> (FastAPI, Flask). <\/p>\n<\/li>\n Familiarity with streaming architectures <\/b> and real\u2011time data handling. <\/p>\n<\/li>\n Knowledge of cloud platforms (Azure preferred) <\/b>, including Azure AI, Cognitive Services, and ML Ops. <\/p>\n<\/li>\n Experience with containerization and orchestration <\/b> (Docker, Kubernetes). <\/p>\n<\/li>\n Understanding of vector databases <\/b> (Pinecone, Weaviate, FAISS) and retrieval mechanisms. <\/p>\n<\/li>\n Experience in CI\/CD, model deployment, and production monitoring. <\/p>\n<\/li>\n<\/ul>\n Exposure to GPU\u2011based inference optimization <\/b> and serverless deployment <\/b>. <\/p>\n<\/li>\n Knowledge of observability and monitoring tools <\/b> for AI (Prometheus, Grafana, Azure Monitor). <\/p>\n<\/li>\n Experience in model fine\u2011tuning <\/b>, prompt engineering <\/b>, or agentic orchestration <\/b>. <\/p>\n<\/li>\n Understanding of AI governance, ethical AI, and data privacy <\/b> frameworks. <\/p>\n<\/li>\n<\/ul>\n Strong analytical and problem\u2011solving mindset. <\/p>\n<\/li>\n Excellent collaboration and communication skills. <\/p>\n<\/li>\n Passion for innovation, experimentation, and applied AI. <\/p>\n<\/li>\n<\/ul>\n #J-18808-Ljbffr<\/p>\n 2026-03-27 08:35:22<\/p>","protected":false},"author":100,"featured_media":0,"comment_status":"open","ping_status":"open","template":"","job_listing_type":[68],"job_listing_category":[158],"job_listing_location":[720],"job_listing_tag":[],"class_list":["post-75905","job_listing","type-job_listing","status-publish","hentry","job_listing_type-full-time","job_listing_category-engineering","job_listing_location-united-arab-emirates"],"metas":{"_job_featured_image":"","_job_featured":"","_job_filled":"","_job_urgent":"","_job_category":{"158":"Engineering"},"_job_type":{"68":"Full 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Skills & Experience <\/b> <\/h3>\n
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Soft Skills <\/b> <\/h3>\n
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