{"id":1945796,"url":"https://alion.io/job/cibeg-ai-engineer-ai-senior-engineer","title":"AI Engineer / AI Senior Engineer","company":{"id":1888126,"name":"CIB Egypt","domain":"cibeg.com","url":"https://alion.io/company/cibeg","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":4,"computed_at":"2026-10-22T06:10:10Z"}},"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":["Giza, Egypt"],"countries":["EG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":94000,"max_usd":243000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2216},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-06T06:53:21Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-22T13:22:12Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"JOB PURPOSE:\nTo develop, test, and deploy Generative AI solutions as part of the AI Engineering team, contributing to the bank’s AI engineering capability. The role is responsible for hands-on implementation across the AI development lifecycle, including model integration, pipeline development, and production deployment, ensuring that AI solutions are robust, scalable, and compliant with established engineering standards.\nWorks closely with Data teams, the AI Solution Architect, and the AI Engineering Manager to translate approved solution designs and model outputs into production-grade AI systems embedded in the bank’s technology ecosystem.\nKEY ACCOUNTABILITIES:\nRoles and Responsibilities\n1. Develop and implement AI engineering components, including model integration pipelines, API wrappers, inference services, and data processing workflows, in accordance with approved solution designs and architectural standards.\n2. Implement LLMOps practices for assigned AI solutions, including model versioning, CI/CD pipeline configuration, automated testing, performance monitoring, and model drift detection.\n3. Develop and maintain APIs and integration components that expose AI model outputs to consuming business applications, ensuring the reliability, performance, and security of AI service endpoints.\n4. Conduct unit and integration testing of AI engineering components, validating that deployed solutions meet functional requirements, performance benchmarks, and governance standards prior to production release.\n5. Monitor AI solutions in production, tracking model performance metrics, system health indicators, and data drift signals, and escalating issues to the AI Engineering Manager where remediation is required.\n6. Contribute to the development and maintenance of reusable engineering components, libraries, and shared tooling that accelerate delivery consistency across AI initiatives.\n7. Collaborate with Data teams to understand model requirements, integration expectations, and performance criteria, providing engineering guidance on deployment constraints and infrastructure considerations.\n8. Maintain technical documentation for AI engineering components, including deployment guides, API specifications, monitoring configurations, and operational runbooks.\n9. Apply secure coding practices and adhere to enterprise security standards throughout AI solution development and deployment activities.\n10. Support the AI Engineering Manager in effort estimation and technical planning for AI initiatives, providing input on implementation complexity, engineering dependencies, and technical risk.\n11. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks.\nPolicies, Processes and Procedures\n12. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner\nDay-to-day Operations\n13. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work\nJOB PURPOSE:\nTo develop, test, and deploy Generative AI solutions as part of the AI Engineering team, contributing to the bank’s AI engineering capability. The role is responsible for hands-on implementation across the AI development lifecycle, including model integration, pipeline development, and production deployment, ensuring that AI solutions are robust, scalable, and compliant with established engineering standards.\nWorks closely with Data teams, the AI Solution Architect, and the AI Engineering Manager to translate approved solution designs and model outputs into production-grade AI systems embedded in the bank’s technology ecosystem.\nKEY ACCOUNTABILITIES:\nRoles and Responsibilities\n1. Develop and implement AI engineering components, including model integration pipelines, API wrappers, inference services, and data processing workflows, in accordance with approved solution designs and architectural standards.\n2. Implement LLMOps practices for assigned AI solutions, including model versioning, CI/CD pipeline configuration, automated testing, performance monitoring, and model drift detection.\n3. Develop and maintain APIs and integration components that expose AI model outputs to consuming business applications, ensuring the reliability, performance, and security of AI service endpoints.\n4. Conduct unit and integration testing of AI engineering components, validating that deployed solutions meet functional requirements, performance benchmarks, and governance standards prior to production release.\n5. Monitor AI solutions in production, tracking model performance metrics, system health indicators, and data drift signals, and escalating issues to the AI Engineering Manager where remediation is required.\n6. Contribute to the development and maintenance of reusable engineering components, libraries, and shared tooling that accelerate delivery consistency across AI initiatives.\n7. Collaborate with Data teams to understand model requirements, integration expectations, and performance criteria, providing engineering guidance on deployment constraints and infrastructure considerations.\n8. Maintain technical documentation for AI engineering components, including deployment guides, API specifications, monitoring configurations, and operational runbooks.\n9. Apply secure coding practices and adhere to enterprise security standards throughout AI solution development and deployment activities.\n10. Support the AI Engineering Manager in effort estimation and technical planning for AI initiatives, providing input on implementation complexity, engineering dependencies, and technical risk.\n11. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks.\nPolicies, Processes and Procedures\n12. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner\nDay-to-day Operations\n13. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work\nQualifications & Experience\n Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Software Engineering, or a related field.\n Senior AI Engineer - 3-5 years of progressive experience in software engineering, AI engineering, or data engineering, with hands-on exposure to developing and deploying AI solutions in production environments.\n AI Engineer - 1-3 years of progressive experience in software engineering, AI engineering, or data engineering, with hands-on exposure to developing and deploying AI solutions in production environments.\n Solid programming proficiency in Python, with working knowledge of AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.\n Experience with LLMOps tooling and practices, including containerization using Docker or Kubernetes, CI/CD pipeline configuration, model versioning, and automated testing workflows.\n Familiarity with LLM integration patterns, including API-based model consumption, prompt engineering, RAG pipeline development, and inference optimization techniques.\n Experience building and maintaining data pipelines and APIs for AI model consumption, with understanding of data transformation, feature engineering, and service integration patterns.\n Familiarity with enterprise security principles, access control, and audit logging requirements relevant to AI system development and production deployment.\nSkills\n Strong hands-on programming capability in Python, with the ability to independently develop, debug, and optimize AI engineering components to production quality.\n Good understanding of machine learning concepts, model lifecycle management, and the operational requirements of AI systems in production environments.\n Working knowledge of DevOps and LLMOps practices, including version control, CI/CD pipelines, containerization, and automated monitoring.\n Analytical problem-solving skills, with the ability to diagnose and resolve engineering issues related to model performance, pipeline failures, and service integration errors.\n Strong collaboration skills, with the ability to work effectively with Data, IT, architecture, and governance teams throughout the AI solution delivery lifecycle.\n Good written communication skills, with the ability to produce clear technical documentation, API specifications, and deployment guides.\n Structured and disciplined approach to code quality, testing, and documentation, with a commitment to engineering standards, reproducibility, and audit readiness\nQualifications & Experience\n Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Software Engineering, or a related field.\n Senior AI Engineer - 3-5 years of progressive experience in software engineering, AI engineering, or data engineering, with hands-on exposure to developing and deploying AI solutions in production environments.\n AI Engineer - 1-3 years of progressive experience in software engineering, AI engineering, or data engineering, with hands-on exposure to developing and deploying AI solutions in production environments.\n Solid programming proficiency in Python, with working knowledge of AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.\n Experience with LLMOps tooling and practices, including containerization using Docker or Kubernetes, CI/CD pipeline configuration, model versioning, and automated testing workflows.\n Familiarity with LLM integration patterns, including API-based model consumption, prompt engineering, RAG pipeline development, and inference optimization techniques.\n Experience building and maintaining data pipelines and APIs for AI model consumption, with understanding of data transformation, feature engineering, and service integration patterns.\n Familiarity with enterprise security principles, access control, and audit logging requirements relevant to AI system development and production deployment.\nSkills\n Strong hands-on programming capability in Python, with the ability to independently develop, debug, and optimize AI engineering components to production quality.\n Good understanding of machine learning concepts, model lifecycle management, and the operational requirements of AI systems in production environments.\n Working knowledge of DevOps and LLMOps practices, including version control, CI/CD pipelines, containerization, and automated monitoring.\n Analytical problem-solving skills, with the ability to diagnose and resolve engineering issues related to model performance, pipeline failures, and service integration errors.\n Strong collaboration skills, with the ability to work effectively with Data, IT, architecture, and governance teams throughout the AI solution delivery lifecycle.\n Good written communication skills, with the ability to produce clear technical documentation, API specifications, and deployment guides.\n Structured and disciplined approach to code quality, testing, and documentation, with a commitment to engineering standards, reproducibility, and audit readiness","description_format":"text","description_chars":11367,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-06T06:53:21Z"}],"visa":[],"liveness":{"score":17,"band":"cold","label":"Long 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