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About Trufla Trufla builds the digital infrastructure that Canadian P&C insurance brokerages run on: quoting, client portals, mobile apps, marketing automation, and the data products behind them. Our customers are brokers dealing with legacy systems, manual workflows, and too many hours in the day spent on work software that should have handled itself. We're a product company, not an agency — what we ship goes straight into the hands of brokers and their clients. Work Smarter, Not Harder: we mean it internally too, and that's a large part of what this role is about. About The Role We're hiring a Senior Software Developer who is strong on the fundamentals and already working the way the industry is heading. The job is still the job: design, build, and run production web software in Node.js and React. But how that work gets done has changed. Our engineers write specifications that agents implement, build the harnesses and evaluation loops those agents run inside, and spend proportionally more of their time on design, review, and verification than on typing out implementations. If you've been doing this genuinely, not just with autocomplete turned on, we want to talk. If you've been skeptical of it and have sharp, specific reasons, we want to talk to you too. What You'll Do Design and ship features end to end across our Node.js services and React front ends, from data model to deployed and monitored. Write specifications precise enough to be executed, covering clear contracts, acceptance criteria, edge cases, and failure modes. Treat those specs as the durable artifact, not a throwaway ticket description. Build and tune the loops: agent harnesses, tool definitions, context strategies, automated verification gates, and evals that tell you whether a change is actually correct before a human ever reads it. Review generated and human-written code with the same rigour, and know where the difference matters. Own production. Instrument what you ship, respond when it misbehaves, and fix the cause rather than the symptom. Raise the floor for the team through patterns, internal tooling, and documented practice that make the next person faster. Work directly with product and with the brokers who use what we build. Context is not filtered through three layers here. What We're Looking For Required 5+ years building and maintaining production software, with real ownership of what you shipped. Deep Node.js and modern JavaScript/TypeScript. You understand the runtime, not just the framework. Strong React, including state management, performance, and component design that survives contact with a growing codebase. Solid grounding in the boring essentials: relational data modelling, HTTP and API design, testing, CI/CD, Git. Working fluency in AI-assisted development as a discipline: spec-driven workflows, agentic development lifecycle practices, and the judgment to know when the loop is producing good work and when it's confidently wrong. Clear written communication. In a spec-driven team this is a core engineering skill, not a soft one. Comfortable collaborating with a distributed team across Canadian and Egyptian time zones. Nice to Have (any one of these is a plus, none are dealbreakers) Cloud and container experience (AWS or GCP, Kubernetes, Terraform). Observability tooling: structured logging, metrics, tracing. Mobile or cross-platform experience (React Native, Swift, Kotlin). Experience building internal developer tooling or frameworks. Insurance, fintech, or another regulated domain. How We Work Small teams, direct ownership, short path from idea to production. Specs and design docs over ad-hoc tickets. Automated verification before human review. We'd rather catch it in the loop. Tooling budget and time set aside to improve how we build, not just what we build. What We Offer Competitive salary Medical insurance, social insurance, and other benefits Hybrid attendance policy Access to the AI tooling you need to do this job properly A team that treats "how we build" as a real engineering problem How to Apply Upload Your Resume, And Complete The Assessment Below https://assessment.trufla.com Trufla is an equal opportunity employer. We welcome applications from all qualified candidates and provide accommodation on request throughout the hiring process. Show more

The Field of Physical Therapy is looking to recruit academic staff in the departments mentioned above. As an academic staff member, you will be expected to teach 4 working days per week including the research day off in the related department and provide academic advising to students. Research is a key objective at Galala University, so you will be expected to carry out high-quality research and scientific collaboration in Physical Therapy that leads to international publications in venues of high standards, and to apply for research grants to generate income and support for the research. As part of GU strategy, Physical Therapy Field at Galala University seeks to appoint highly motivated academic staff for a full-time and part-time positions in three departments. Qualifications PhD in Physical Therapy in the requires specializations. At least 3-year teaching experience in higher education after PhD A strong track record of research. Proven high-quality communication skills in English, both written and oral. Teaching and/or research awards (e.g., best paper awards). Membership of professional bodies. A strong track record of professional and community services locally and globally. Applicant must have a doctorate degree (PhD) in Physical Therapy from an Egyptian or foreign university equivalent to the Supreme Council of Universities. Responsibilities The duties and responsibilities include but are not limited to the following: you will be expected to teach 4 working days per week including the research day off. Delivering lectures and associated lab sessions. Conducts different interactive teaching methods. Contributes to the development of the curriculum, professional, educational quality assurance (QA), and research profile of the university. Supervises students’ projects, dissertations, and/or placements as appropriate. Supporting and promoting the field and departmental missions centered on excellence in teaching and learning through quality instruction and curriculum development. Participating in faculty committees, departmental meetings, research, and community service. Developing of syllabi for relevant courses and co-curriculum activities. Contributing to the improvement of the quality of instruction by utilizing innovative and interactive methodologies, techniques, and delivery methods. Providing students with frequent and timely assessment and clear feedback regarding academic performance Holding regular office hours. Participating in foundational activities and duties relevant to the mentioned specialties mentioned above program/Faculty of Physical Therapy Performing other duties as assigned. “Field will state their points as per each job requirements” Requirements – Interested applicants are invited to submit the documents listed below through the application with an updated photo (PDF format): Curriculum Vitae Teaching Statement Research Statement (describe current research agenda) Letter of reference (two letters of references, at least one of which should speak about the candidate’s teaching and research abilities. Show more

Job Summary The DevOps Engineer at CEQUENS is instrumental in bridging the gap between development, operations, and IT through continuous development, integration, and deployment practices. This role involves implementing automation tools, managing CI/CD pipelines, and ensuring high availability and performance of the applications. The DevOps Engineer collaborates closely with software developers, system operators, and other IT staff to oversee code releases and deployments. The right candidate will be based in Egypt but you will be able to work remotely in Egypt. Main Areas of Responsibility: Continuous Integration and Deployment Develop, maintain, and improve the CI/CD pipeline to automate build, test, and deployment processes Implement and manage tools that integrate, automate, and enhance the development and deployment processes System Management and Monitoring Monitor, troubleshoot, and optimize performance of the IT infrastructure Ensure system reliability and availability through proactive infrastructure management and scaling strategies Automation and Scripting Automate repetitive tasks through scripting and other automation tools to improve efficiency and reduce the risk of human error Develop automation scripts to manage configurations, backups, and system monitoring Collaboration and Support Work closely with development teams to integrate new deployment processes and strategies for improving server and application performance Provide technical guidance and support to other team members, helping resolve complex technical issues Security and Compliance Implement security measures throughout the infrastructure and development lifecycle to protect sensitive data and systems Ensure compliance with security regulations and company policies Requirements Education Bachelor's degree in Computer Science, Information Technology, or a related field Experience 2-5 years of experience as a DevOps Engineer or similar role in a software engineering or IT operations environment Experience with tools such as Git, Docker, Kubernetes, Ansible, Terraform, and other cloud-native technologies Skills Proficiency in scripting languages like Python, and Bash Strong knowledge of Linux/Unix administration Experience with cloud services AWS and managing serverless architectures Strong analytical and troubleshooting skills, with a systematic approach to problem-solving Effective communication skills, capable of collaborating with multi-disciplinary teams Show more

Position Senior Data Engineer Job Description Seeking an experienced Senior Data Engineer to design, development, and optimization of our data pipelines, data models, and analytics infrastructure. The ideal candidate has strong experience in building scalable data platforms across operational systems and enterprise data warehouses. Key Responsibilities Core Responsibilities Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data. Develop high-quality data integration workflows using modern data engineering tools and frameworks. Optimize data processing performance, storage usage, and pipeline reliability. Implement data quality checks, validation rules, and monitoring dashboards. Collaborate with data analysts, data scientists, and business teams to deliver reliable datasets suitable for reporting and analytics. Manage data security, access controls, and governance best practices. Support production workflows, troubleshoot issues, and improve platform stability. Additional Responsibilities for Data Models & Structures (Operational + DW) Own the design and maintenance of logical and physical data models for operational systems and enterprise data warehouse (OLTP + DWH). Define and enforce data modeling standards, naming conventions, and schema governance. Translate business processes into efficient database schemas, including star/snowflake models. Optimize queries and database structures for high-performance analytics and real-time data access. Work closely with DBAs, architects, and product teams to ensure data models meet scalability and performance requirements. Lead database refactoring efforts, schema evolution, and versioning processes. Perform impact analysis for schema changes across upstream and downstream systems. Requirements Must-Have Requirements 5+ years of experience as a Data Engineer or similar role. Strong Python Skills and Experience Strong SQL /PLSQL skills and experience with relational databases (Oracle, PostgreSQL, MySQL, or similar). Experience with NoSQL databases and streaming systems (Kafka, Kinesis, etc.). Hands-on experience with data processing frameworks (Spark, Databricks, or similar). Proficiency with cloud platforms (AWS). Experience with workflow orchestration (Airflow, Prefect, or similar). Strong understanding of ETL/ELT patterns, data warehousing concepts, and BI fundamentals. Proven ability to design robust data models and optimize database structures. Knowledge of data governance, cataloging, and metadata management. Nice-to-Have Skills Familiarity with CI/CD, infrastructure-as-code, and DevOps practices. Experience with real-time analytics or high-volume distributed data systems Location: EG-Cairo, Egypt (Al Emdad & Al Tamween) Time Type Full time Job Category Information Technology Show more

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 B2B commerce is evolving fast and Sana Commerce is built to lead it. We help manufacturers, distributors, and wholesalers create seamless digital buying experiences that strengthen customer relationships and drive real business growth. We're on a mission to become the go-to commerce platform for B2B companies navigating complexity at scale. That means constantly pushing the boundaries of what's possible in ERP-integrated commerce and building a team bold enough to get us there. At Sana Commerce, we’re looking for an AI Engineer who can help us build a high performance, scalable e-commerce platform. You will be playing a critical role in expanding our product at speed, while building features that help our customers grow, and contributing to our initiative of bringing engineering excellence to a new level. Who we are: Champions of Our League. "We deliver lasting success, balancing quick wins and long-term value." We take pride in our unique product and extensive B2B knowledge and continuously strive to improve. No matter our role, we bring value every day, helping our customers and partners succeed. Supercharge Our Customers. "We’re revolutionizing B2B commerce together, helping our customers to lead and succeed." Our customers are at the heart of everything we do. We go beyond solutions, providing the tools and support they need to grow. Determined to Grow. "We embrace challenges, growing and raising the bar for ourselves and our industry." We take on challenges, seek feedback, and keep learning. Every setback is a chance to improve and move forward. Bold Together. "We dare to be bold because we have each other’s back." We collaborate across teams and time zones, challenge the status quo, and support each other to achieve the best outcomes. Job Description What you'll be doing Design, develop, and deliver AI-powered features in Sana Commerce – agents that act on real commerce data (catalog, pricing, inventory, orders) Work with modern LLM frameworks and toolchains (e.g. any of Lang*, AWS Bedrock, MS Foundry, Cloudfare Agents, etc…)[VL1.1] to build RAG systems, AI Agents, and dynamic workflows – while understanding the raw agentic loop underneath them Work in a cross-functional team (Engineering, Product, Design) to integrate AI seamlessly into a distributed product Apply solid AI engineering principles end to end, from context and tool design through evaluation, deployment and continuous improvement Build the eval and feedback loops that gate releases: curated datasets, metrics and thresholds that block a regression the way a failing test does Design for failure by building the recovery path before the happy path Contribute to LLMOps and AI observability, improving reliability, evaluation, and monitoring pipelines Stay on top of emerging AI tools and frameworks, helping shape the vision and roadmap of Sana Commerce. Qualifications What you bring Proven commercial experience building and deploying production-grade AI applications. 4+ years of web application engineering experience Strong software engineering (back-end, front-end nice to have) and SDLC fundamentals (design patterns, SOLID principles, experience with building complex MACH systems, CI/CD, testing) Apply strong backend and system design principles to ensure deterministic behaviour where needed, clear failure handling, human-in-the-loop controls, and production readiness. Practical knowledge of LLMs, their components (tokenization, attention, embeddings, vector search), and common limitations Hands-on experience with RAG pipelines, agents, and Vector Stores and the judgement to know when retrieval is the wrong tool Familiarity with LLMOps practices, monitoring, evaluation, and feedback loops. Has built real evals that are tied to business impact– dataset, metrics, thresholds and can name the regression they caught Cost and latency literacy – can say what a run costs and how to halve it Strong working knowledge of Cloud technologies. Ready to build reliability that scales? Apply now and help shape the foundation of our next-generation SaaS platform. Show more