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AI Engineer – Agentic AI & LLM Applications Job Summary We are seeking an AI Engineer to help transition enterprise AI capabilities from prototype to production. In this role, you will design, build, and deploy robust Agentic AI systems capable of planning, reasoning, and executing complex business workflows. You will combine strong software engineering skills with hands-on expertise in LLMs, AI agents, RAG, orchestration frameworks, tool calling, evaluation, observability, and LLMOps. You will also experiment with emerging models, frameworks, and tools to identify technologies that can deliver measurable business value. The ideal candidate is a hands-on and forward-thinking engineer who enjoys experimentation but understands what is required to operate AI systems reliably in production. You will work closely with product managers, business stakeholders, data engineers, and platform teams to deliver AI solutions that address real-world enterprise needs. Project Overview About the Client Our client is a leading multi-brand technology solutions provider serving business, government, education, and healthcare customers across the United States, United Kingdom, and Canada. The organization provides a broad range of technology offerings, spanning hardware and software through integrated IT solutions including security, cloud, data center, and networking. About the Project The project focuses on advancing enterprise AI capabilities from experimentation and prototyping into reliable, scalable, production-grade Agentic AI solutions. The team is developing AI agents capable of interacting with enterprise systems, retrieving and reasoning over business data, invoking tools, maintaining state, and executing multi-step workflows with appropriate human oversight. This role provides an opportunity to work at the intersection of AI engineering, software engineering, LLM applications, RAG, agent orchestration, and production AI operations. Key Responsibilities Agentic AI Engineering & Orchestration Architect, design, develop, and deploy complex Agentic AI workflows using modern AI technologies and the Microsoft AI ecosystem. Build multi-agent systems capable of handling complex workflows, loops, interruptions, and human-in-the-loop interventions. Design reliable agent orchestration patterns using frameworks such as LangChain, LangGraph, or equivalent technologies. Develop tool-use and function-calling capabilities that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms. Integrate agents with enterprise platforms such as Salesforce, Workday, ServiceNow, and other business systems. Design agent state-management solutions supporting memory, conversational history, context management, and persistence. Implement appropriate safeguards around agent actions, tool access, and autonomous execution. LLM & Model Engineering Integrate and evaluate commercial and enterprise LLM APIs, including OpenAI, Azure OpenAI, Anthropic, Gemini, and similar providers. Experiment with emerging models, agent frameworks, tools, and techniques to identify opportunities for enterprise adoption. Design prompts and agent instructions using advanced techniques such as ReAct, few-shot prompting, structured outputs, and other appropriate approaches. Optimize applications to handle model limitations including rate limits, context-window constraints, latency, non-deterministic responses, and transient failures. Evaluate model performance across different use cases and select appropriate models based on quality, latency, cost, and business requirements. RAG & Data Engineering Design and implement production-grade Retrieval-Augmented Generation (RAG) pipelines. Optimize document ingestion, chunking, embeddings, vector indexing, retrieval, and re-ranking strategies. Work with vector databases such as Pinecone, Weaviate, pgvector, or equivalent technologies. Collaborate with Data Engineering teams to develop and maintain high-quality golden datasets for agent and RAG evaluation. Implement strategies to improve retrieval accuracy, relevance, freshness, and context quality. Ensure data pipelines support reliable and secure consumption by AI agents. LLMOps, Evaluation & Quality Develop automated evaluation pipelines for AI applications and agents. Implement LLM-as-a-Judge and other evaluation approaches to measure accuracy, relevance, hallucination, safety, and task completion. Integrate AI quality gates into CI/CD pipelines to prevent poorly performing agent versions from reaching production. Establish evaluation datasets, benchmarks, regression tests, and quality metrics for AI systems. Implement production observability and tracing to monitor agent workflows, model calls, tool usage, latency, errors, and failures. Use tracing and observability platforms to investigate agent behavior and troubleshoot production issues. Monitor token consumption, model usage, latency, and infrastructure costs. Optimize prompts, context size, caching, model selection, and application architecture to improve cost and performance. Production Engineering Transform AI prototypes and proof-of-concepts into maintainable, scalable, production-ready applications. Apply software engineering best practices including testing, version control, code reviews, documentation, CI/CD, and automated quality controls. Design systems that account for the probabilistic nature of AI while maintaining predictable business outcomes. Implement appropriate error handling, retries, fallbacks, validation, and human-in-the-loop controls. Ensure AI applications meet enterprise requirements for reliability, security, scalability, and maintainability. Innovation & Collaboration Evaluate emerging AI models, frameworks, agent protocols, and technologies. Identify opportunities to standardize emerging technologies across the enterprise. Collaborate with Product Managers and business stakeholders to translate business problems into practical AI solutions. Communicate technical concepts and AI limitations clearly to non-technical stakeholders. Document architecture, technical designs, implementation decisions, and operational practices. Share knowledge and establish engineering best practices across the AI development community. Required Skills & Experience Core Engineering Bachelor's degree with 5+ years of software engineering experience, including exposure to AI/ML applications; or 9+ years of software engineering experience with exposure to AI/ML applications. Strong hands-on Python development experience. Strong software engineering fundamentals, including APIs, distributed systems, testing, version control, and production application development. Experience building and deploying production software rather than only prototypes or proof-of-concepts. Agentic AI Hands-on experience designing and building AI agents / Agentic AI solutions. Experience implementing multi-step agent workflows, tool calling, function calling, and agent orchestration. Experience with LangChain, LangGraph, or comparable agent orchestration frameworks. Understanding of agent state, memory, context management, and human-in-the-loop patterns. LLM Engineering 2+ years of hands-on experience building applications with LLMs. Experience integrating LLM APIs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or equivalent. Strong understanding of prompt engineering and LLM application design. Experience handling production challenges including rate limits, context-window limitations, latency, failures, and non-deterministic outputs. RAG & Vector Databases Experience designing and implementing RAG solutions. Hands-on experience with vector databases such as Pinecone, Weaviate, pgvector, or equivalent. Understanding of embeddings, chunking, vector search, metadata filtering, retrieval optimization, and re-ranking. LLMOps & Production AI Experience implementing AI evaluation and testing strategies. Experience with automated evaluation, regression testing, or LLM-as-a-Judge approaches. Experience with AI observability, tracing, and production monitoring. Understanding of AI cost optimization, including token usage, caching, prompt optimization, and model selection. Preferred Skills Experience with Microsoft AI technologies and the Azure AI ecosystem. Experience with Azure OpenAI. Experience with enterprise SaaS integrations such as Salesforce, Workday, or ServiceNow. Experience with agent evaluation frameworks and AI observability platforms. Experience implementing multi-agent architectures. Experience with structured outputs and tool/function calling. Experience with AI security, guardrails, and responsible AI practices. Experience with CI/CD and MLOps/LLMOps platforms. Experience evaluating emerging agent protocols and connectivity standards. Experience with Kubernetes and cloud-native application deployment. Technical Environment Language: Python LLMs: OpenAI, Azure OpenAI, Anthropic, Gemini Agent Frameworks: LangChain, LangGraph, and similar frameworks Vector Databases: Pinecone, Weaviate, pgvector AI Architecture: Agentic AI, Multi-Agent Systems, RAG Enterprise Integration: APIs, SaaS platforms, databases, enterprise applications AI Quality: LLM-as-a-Judge, evaluation datasets, regression testing Observability: AI tracing, distributed tracing, application monitoring Cloud: Microsoft Azure and enterprise cloud environments Engineering: Python, Git, CI/CD, automated testing Key Competencies Agentic AI Architecture LLM Application Development Python Engineering RAG & Retrieval Engineering Agent Orchestration Tool & Function Calling LLM Evaluation AI Observability Production AI Engineering AI Cost & Performance Optimization Education Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, or a related technical discipline is preferred. Why This Position? This is an opportunity to work on the transition from AI experimentation to enterprise-scale production AI. You will have the opportunity to: Build sophisticated Agentic AI systems that solve real business problems. Work hands-on with rapidly evolving LLMs, models, and AI frameworks. Design production-grade architectures rather than isolated AI prototypes. Influence enterprise standards for agent orchestration, RAG, evaluation, and LLMOps. Collaborate with product, business, data, platform, and engineering teams. Help define how AI agents are safely and reliably deployed across the enterprise. Show more

Company Description Founded in 1919, The American University in Cairo (AUC) moved to a new 260-acre state-of-the-art campus in New Cairo in 2008. The university also operates in its historic downtown facilities, offering cultural events, graduate classes, and executive and continuing education. Student housing is available in New Cairo. Among the premier universities serving Egypt and the region, AUC is accredited by the Middle States Commission on Higher Education. In addition, the Association to Advance Collegiate Schools of Business (AACSB), The European Foundation for Management Development (EFMD)'s EQUIS, and the Association of MBAs (AMBA) accredit the School of Business; ABET accredits its engineering programs; the Canadian Society for Chemistry accredits the chemistry program; and the Master of Public Administration and the Master of Public Policy programs of GAPP are accredited by the Network of Schools of Public Policy, Affairs, and administration (NASPAA). The AUC libraries contain the largest English-language research collection in the region. They are an active and integral part of the university's pursuit of excellence in all academic programs. AUC is an English-medium institution; eighty-five percent of the students are Egyptian. The rest include students from nearly ninety countries, mainly from the Middle East, Africa, and North America. Faculty salary and rank are based on qualifications and professional experience. According to AUC policies and procedures, all faculty are entitled to generous benefits. About The Onsi Sawiris School Of Business The roots of the Onsi Sawiris School of Business date back to 1947, when the first degree in economics was introduced in Egypt. The school has been accredited by the Association to Advance Collegiate Schools of Business (AACSB) since 2006, the European Foundation for Management Development (EFMD)'s EQUIS, and the Association of MBAs (AMBA) since 2014, when it became among the 1% triple-crown accredited business schools in the world. The school is a member of the Global Alliance in Management Education (CEMS) and the Global Network for Advanced Management (GNAM). In 2024, the school was ranked by Eduniversal as the best in Africa for the 8th consecutive year and 73rd globally in open enrolment in executive education by the Financial Times. The school offers six undergraduate degrees, including bachelor's degrees in economics, accounting, marketing, finance, information and communication technology management, and entrepreneurship. In addition, the school offers six graduate degrees in economics, economic development, finance, and international management, as well as an MBA and an EMBA. The school is home to 8 research and outreach centers, including the Access to Knowledge for Development Center, the Center for Entrepreneurship and Innovation, the Venture Lab, and the Al-Khazindar Business Research and Case Center. For more information about the school: business.aucegypt.edu. Job Description The American University in Cairo's Onsi Sawiris School of Business invites nominations and applications for a faculty position in the Mohamed Shafik Gabr Department of Economics at the level of Assistant/Associate/Professor for a fixed-term four-year appointment, to begin in Fall 2027. The department seeks an applicant with exceptional teaching and research records in Quantitative and Applied Econometrics/Economics. We particularly encourage candidates whose work combines strong econometrics and quantitative methods with applied research, including the use of Machine Learning, Data Science, and computational tools to address contemporary economic questions. The applicant must build a diverse research team in collaboration with local and international participants, addressing how the above sub-disciplines relate to the Middle East and African regions. Strong practical quantitative, econometric, data science, and research methodology skills are essential. The successful applicant will be expected to contribute to undergraduate and graduate teaching, and must be actively engaged in research and service, and supervise master's theses. Finally, all faculty must be willing to teach introductory courses. Requirements Applicants must hold a PhD in economics or finance from a reputable university, preferably one accredited by AACSB and/or EQUIS. Applicants must demonstrate an internationally recognized record of research excellence and scholarly impact. Applicants must have a proven record of research publications in the past five years in top-tier journals (A*/A according to the ABDC ranking or Q1/Q2 according to Scopus ranking). PhD candidates must demonstrate a strong research agenda and a history of securing competitive research funding. Candidates should demonstrate commitment to academic research, teaching, and service. Key Responsibilities Research & Scholarship Leading a world-class research program in quantitative and applied econometrics. Securing competitive external funding to sustain and expand research initiatives. Conducting impactful research. Publishing in top-tier journals and presenting at leading international conferences. Engage in international research projects with partner universities and institutions. Teaching & Mentorship Developing innovative undergraduate and graduate curricula that incorporate rigorous and advanced quantitative methods and their applications to business and economics. Aligning degrees with changing market demands. Delivering graduate and undergraduate courses in statistics, econometrics, applied quantitative methods, and related applied courses. Supervising and mentoring graduate students. Mentoring faculty. Institutional Leadership Serving as a catalyst for interdisciplinary research across departments, schools, and external partners. Representing the school and university in national and international forums, strengthening its global reputation, and elevating the level of collaboration. Community & Industry Engagement Building partnerships with industry, government, and civil society to translate research into real-world impact. Engaging in public discourse on the application of quantitative methods to inform governments and industry. Collaborating with industry to design applied research projects. Additional Information Review of applications will begin immediately and continue until the position is filled. Only shortlisted candidates will be contacted. Application Instructions All applicants must submit the following documents online: Cover letter outlining research, teaching, and leadership vision. Current Curriculum vitae. Statements of research and teaching. Sample of current research (up to three representative publications). Evidence of teaching effectiveness. Completed AUC Personnel Information Form (PIF); and, Three names of reference to busref@aucegypt.edu with the subject line BUS/Econ/2027 Apply Here PI287114046 Show more

We are seeking a highly motivated and experienced AI/ML Developer Level II to join our dynamic team. In this role, you will be a key contributor to the design, development, and deployment of sophisticated conversational AI systems, primarily using the RASA framework. Your deep expertise in Python, coupled with hands-on experience in the Google Cloud Platform (GCP) ecosystem, will be essential for building, scaling, and maintaining robust, enterprise-grade virtual assistants and chatbots. You will move beyond prototyping to take ownership of components, optimize model performance, and ensure the reliability of our AI solutions in production. Key Responsibilities (Must-have) RASA Framework Development: Design, build, and maintain advanced conversational AI agents using the RASA Open Source and/or RASA X/Pro platforms. This includes developing complex dialogue management with stories and rules, configuring the NLU pipeline, and creating custom actions. Model Training & Optimization: Train, evaluate, and fine-tune RASA NLU and dialogue models. Implement strategies for continuous improvement using conversation analytics and user feedback to enhance intent classification, entity recognition, and response quality. Python-Centric Solutioning: Write clean, eƯicient, and well-documented Python code for custom actions, policies, and integrations. Develop scalable backend services and APIs to connect RASA agents with other business systems. Google Cloud Platform (GCP) Integration & Deployment: Architect, deploy, and manage RASA bots on GCP (using Google Kubernetes Engine - GKE, Pub/Sub for messaging, Cloud Run, or Compute Engine). Utilize GCP services like Vertex AI and Dialogflow CX for complementary use-cases or hybrid architectures, and Cloud Speech-to-Text / Text-to-Speech for voice-enabled bots. Nice-to-have: CI/CD & MLOps: Implement and maintain CI/CD pipelines for automated testing, building, and deployment of RASA models using tools like Git. Champion MLOps best practices for versioning, monitoring, and retraining models. Data Management: Leverage Google BigQuery for analyzing conversation logs and deriving insights. Use Cloud Storage for managing training data and model artifacts. Required Qualifications: Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience. Experience: 3+ years of professional experience in AI/ML development, with at least 2 years of hands-on, in-depth experience building and deploying production-level chatbots with the RASA framework. Programming: Strong proficiency in Python, with a solid understanding of software engineering principles, design patterns, and API development. Google Cloud Platform: Proven, hands-on experience with core GCP services, including: Compute: Google Kubernetes Engine (GKE), Cloud Run, or App Engine. AI/ML Services: Practical knowledge of Dialogflow and/or Cloud Natural Language API. Infrastructure: Cloud Storage, Cloud Build, IAM, and VPC networking. Machine Learning Fundamentals: Solid understanding of NLP fundamentals (intent detection, entity extraction, context management) and practical experience with machine learning libraries (e.g., scikit-learn, spaCy, Transformers). Version Control & Collaboration: High proficiency with Git in a collaborative team environment. Soft Skills & Other Requirements: Problem-Solving: Excellent analytical and problem-solving skills with the ability to troubleshoot complex technical issues in distributed systems. Ownership & Initiative: A proactive mindset with the ability to take ownership of projects from conception to deployment and beyond, working with minimal supervision. Communication: Strong verbal and written communication skills. Ability to clearly articulate technical concepts to both technical and non-technical stakeholders. Agile Methodology: Experience working in an Agile/Scrum development process. Team Player: A collaborative attitude, with a willingness to mentor junior developers and share knowledge with the team. Continuous Learning: A passion for staying up-to-date with the rapidly evolving fields of Conversational AI, MLOps, and cloud technologies. Preferred Qualifications (Bonus): GCP Professional Machine Learning Engineer or other GCP certifications. Experience with containerization technologies (Docker) and orchestration (Kubernetes). Knowledge of infrastructure-as-code tools like Terraform. Show more

About Payrails Payrails is a global payment software company helping leading enterprises to take control of their payment operations and maximize performance. With deep experience in the payments space and firsthand knowledge of merchants' challenges, we’ve seen how fragmented, complex, and inefficient these systems can become. Now, we are setting a new industry standard for how enterprises around the world manage and optimize payments, with more control, visibility and flexibility than ever before. Our vision is to reimagine payments from the ground up. We are building a deeply integrated meta layer that spans the entire payment lifecycle with a modular architecture. This helps us give leading brands building blocks to craft solutions to most complex operational challenges, tailor seamless customer experiences and grow their business. We are backed by some of the world’s top investors including Andreessen Horowitz, HV Capital, EQT Ventures and General Catalyst, who share our mission to simplify payment complexity globally. At Payrails, we’re committed to building a team of exceptional people - not just talented, but driven to build, solve, and deliver at the highest level. Excellence isn’t just a value here; it’s a way of working. We believe that great people thrive in environments where there’s trust, clarity, and shared purpose. We work openly and collaboratively, with deep respect for each other’s craft. Everyone is encouraged to understand the bigger picture - what we’re building, why it matters, and what stands in the way. When people have that context, they move faster, take ownership, and make better decisions. We care about creating a culture where people feel inspired to do their best work and a deep sense of responsibility to help us bring our vision to life. Success at Payrails means staying focused on the problems that matter most, and executing with purpose. Your team At Payrails, data is at the core of everything we do. We don’t just analyze data; we turn it into actionable insights that fuel smarter decision-making and drive the future of our products. As we continue to revolutionize the payments industry we are seeking an exceptional Senior Data Engineer to help us in our mission. Our tech team offers you the chance to work alongside a group of highly ambitious and knowledgeable Engineers, while being faced with complex problems that affect customers on a global scale. How you will make an impact Work alongside a talented team of analysts, data scientists and engineers to build data products and services that meet business requirements. Design, develop and maintain robust, scalable and observable batch and real-time data pipelines. Architect, build and maintain the data infrastructure at Payrails. Advocate for and implement data governance and security best practices. Contribute to tooling that improves data quality, CI/CD workflows, and data observability. Collaborate with cross-functional teams to understand requirements and design optimal solutions. What we are looking for You have 5-7 years of extensive experience in data engineering roles building reliable, scalable and observable production-grade data pipelines and services. You have strong proficiency in Python. Experience with GoLang is a plus. You are proficient in SQL, data modelling and operating common OLTP (e.g. PostgresSQL) and OLAP databases (e.g. Snowflake, Bigquery) at scale. You develop and maintain data pipelines using workflow management platforms such as Apache Airflow. You are knowledgeable of containerization technologies such as Docker and container orchestration platforms like Kubernetes. You communicate effectively and are proficient in English. You work independently and collaboratively in a fast-paced environment. Experience with real-time streaming technologies (e.g. Kafka, Kinesis) and distributed stream processing technologies (e.g. Apache Flink, Apache Beam, Kafka Streams) is an advantage. Familiarity working with infrastructure as code (e.g. Terraform) is preferred. FinTech or payments domain experience is a plus. What we offer High impact and high velocity environment with the most talented and ambitious people you will ever work with A chance to shape the story of a company and a category from the ground up Real ownership. You’ll have the freedom and trust to build, test, and take lead A product used by the best brands around the world Hybrid working with an office in the heart of New Cairo Regular team events, activities and off-sites Competitive salary and equity package 27 days of annual paid vacation Show more

SYSTRA is one of the world's leading engineering and consultancy groups specialising in public transport and sustainable mobility. With over 10,300 employees, SYSTRA's mission is to design safe and sustainable transport solutions to bring people together, develop social inclusion and facilitate access to employment, education and leisure throughout the world. For 65 years, the Group has been working alongside cities and regions to contribute to their development by creating, improving and modernising their infrastructure and transport systems, throughout the life cycle of their projects. SYSTRA is involved from the earliest stages of design through to the testing, deployment and maintenance phases. The company provides all its services in over 80 countries worldwide and generates 74% of its turnover internationally. With its new services, SYSTRA supports its clients and partners in their digital, ecological and energy transition, in order to invent the mobility of tomorrow. Context The Logistics Coordinator is responsible for monitoring company vehicles, ensuring their operational readiness, coordinating maintenance and documentation, and reviewing fleet-related invoices and expenses. The role supports smooth daily operations, cost control, and accurate record management. Missions/Main Duties Monitor the daily movement and usage of company vehicles. Ensure vehicles are properly maintained and available when needed. Track renewals for licenses, insurance, and inspections. Coordinate routine and emergency maintenance with suppliers or workshops. Review and verify fuel, maintenance, and other fleet-related invoices. Coordinate with the Finance Department for invoice processing and payment. Maintain accurate records for vehicles, repairs, and expenses. Prepare regular reports on fleet status, costs, and operational issues. Follow up on traffic violations, accidents, and related administrative actions. Perform any other work-related duties assigned by the direct manager. Profile/Skills Bachelor’s degree or relevant diploma in Logistics, Business Administration, Accounting, or a related field. 1–3 years of experience in logistics, fleet management, or invoice follow-up. Good organizational and communication skills. Strong attention to detail and accuracy. Good knowledge of Microsoft Office, especially Excel. Systra is an equal opportunities company; this position is open to all applicants. Workplace Type On-site Show more

We at Yara are part of a global network, collaborating to profitably and responsibly solve some of the world's key challenges - resource scarcity, food insecurity and environmental change. Responsibilities About the Unit Profile Additional Information Contact details Apply no later than Knowledge grows through differences Yara is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. We believe that creating a diverse and inclusive work environment is not only the right thing, but also the smart thing to do. To deliver on this, Yara has firmly anchored Diversity, Equity & Inclusion (DE&I) in our business strategy and has more than 400 employees worldwide involved in D&I ambassadors networks. As part of our recruitment process, where permitted by local law, we may conduct reference and background checks. These checks will only be performed when deemed necessary for the nature of the job. Candidates will be informed by HR before any background checks are initiated. Show more