Senior AI Engineer – LLM, RAG, Agent Systems

SSC HR Solutions

. Design and build production-grade LLM applications and RAG systems .

Posted 9/18/2026full-timeRemote • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building production-grade LLM applications and retrieval architecture, with a strong focus on optimization and evaluation frameworks. Proficient in Python and experienced in deploying self-hosted models while collaborating effectively with data and software engineering teams.

Highest-signal resume keywords

Production LLM-Based Systems DevelopmentPython Engineering SkillsRetrieval Architecture ExpertiseLLM Evaluation FrameworksSelf-Hosted Model Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringData EngineeringChunking StrategiesEmbeddingsVector DatabasesHybrid SearchRerankingInference OptimizationFine-TuningText-to-SQL

Tools & Technologies

VLLMModel-Serving InfrastructureGPU Resource ManagementRegression TestingNatural-Language Interfaces

Industry Keywords

RAG SystemsAI Systems GuardrailsEnterprise DataStructured DatabasesAnswer-Quality Evaluation

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Design and build production-grade LLM applications and RAG systems
Own retrieval architecture, including chunking, embeddings, vector search, hybrid search, and reranking
Build agentic and tool-calling systems with permissions, scoping, validation, and guardrails
Develop natural-language interfaces over enterprise data and structured databases
Build and maintain LLM evaluation frameworks with test sets, regression suites, grounding, hallucination, and answer-quality evaluation
Work within the data platform and engineering stack rather than relying solely on hosted AI APIs
Deploy and optimize self-hosted open-weight models using vLLM or equivalent serving infrastructure
Optimize inference performance, GPU utilization, latency, throughput, and cost
Explore and implement fine-tuning or model adaptation when appropriate
Collaborate with data and software engineers to turn AI capabilities into reliable production products

Requirements

What you’ll need

5+ years of software or data engineering experience
At least 2 years of hands-on experience building and deploying production LLM-based systems
Strong Python engineering skills
Deep understanding of RAG and retrieval architecture, including chunking strategies, embeddings, vector databases/search, hybrid search, reranking, and retrieval evaluation
Experience building LLM agents or tool-calling systems
Understanding of permissions, access control, scoping, validation, and guardrails for AI systems
Strong understanding of LLM evaluation, including test datasets, regression testing, grounding, and hallucination detection
Experience working directly with data platforms, databases, or enterprise data
Strong software engineering fundamentals and experience taking systems from prototype to production
Experience with self-hosted open-weight models, vLLM or equivalent model-serving infrastructure, GPU resource management, inference optimization, fine-tuning, LoRA, Text-to-SQL, semantic layers, and combining unstructured documents with structured enterprise data (strongly preferred)