Qureos Mentor

malomatia

<div>
 
 Job Description
 
 
 
 We are seeking a skilled
 
 Application Security Specialist
 
 to join our Cybersecurity Practice. In this role, you will work closely with our application security, development, and QA teams to secure our clients&rsquo; applications across their entire lifecycle. You will conduct security testing, perform penetration tests, and assess vulnerabilities across web, mobile, API applications.
 
 
 Your responsibilities will span application penetration testing, automated security scanning (SAST, DAST, SCA), threat modelling, secure code review, and developer enablement. You will embed security best practices throughout the software development lifecycle and ensure that applications are designed and built with robust security controls.
 
 
 You will collaborate with development and DevOps teams to integrate security into CI/CD pipelines, triage and validate findings, and provide clear, actionable remediation guidance. The role requires hands-on technical expertise, strong analytical skills, and the ability to translate complex security findings into practical fixes that developers can implement.
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Penetration Testing:Conduct penetration tests on web applications, mobile applications, APIs, and thick-client applications. Prepare detailed reports with clear risk ratings and actionable remediation recommendations.
 </li>
 <li>
 Security Scanning:Implement, tune, and manage automated security scanning tools (SAST, DAST, SCA) to continuously identify vulnerabilities in code, configurations, and third-party dependencies across all application types.
 </li>
 <li>
 Threat Modelling:Perform threat modelling to identify potential security risks and attack surfaces associated with applications early in the design process, and provide guidance on mitigating these risks.
 </li>
 <li>
 Secure Code Review:Review application source code for security vulnerabilities across multiple platforms and languages, and offer practical, developer-friendly recommendations for remediation.
 </li>
 <li>
 DevSecOps Integration:Integrate security testing and controls into CI/CD pipelines, enabling continuous and automated security validation as part of the development workflow.
 </li>
 <li>
 Training &amp; Awareness:Develop and deliver secure coding training sessions and workshops to raise application security awareness among development teams and other stakeholders.
 </li>
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 Tool Evaluation:Assess and recommend tools and technologies to enhance application security testing and monitoring capabilities across various platforms.
 </li>
 <li>
 Documentation:Create and maintain comprehensive documentation related to security assessments, vulnerability management, and application security standards and policies.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Education: Bachelor&rsquo;s / college degree in Computer Science, Information Security, or a related field.
 </li>
 <li>
 Experience: At least 3 years of experience in application security, secure software development, penetration testing, or a closely related field.
 </li>
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 Certifications: Relevant professional certifications are highly desirable. These may include, but are not limited to:
 </li>
 <ul>
 <li>
 OffSec certifications (e.g., OSWA, OSWE)
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 <li>
 eLearnSecurity (e.g., eWPT, eWPTX)
 </li>
 <li>
 GIAC / SANS (e.g., SEC542, GWAPT)
 </li>
 <li>
 BCSP or other application security certifications.
 </li>
 </ul>
 <li>
 Technical Skills: Proficiency with security testing tools such as Burp Suite (required), and familiarity with Snyk, HCL AppScan, Fority, and Postman (preferred). Strong understanding of secure coding practices and hands-on experience with at least one programming language. Familiarity with DevSecOps practices and CI/CD pipelines is preferred.
 </li>
 <li>
 Knowledge: In-depth knowledge of application security principles and practices, with strong familiarity with security frameworks and guidelines (e.g., OWASP Top 10, ASVS, MASVS, WSTG, MSTG). Understanding of common vulnerability classes, exploitation techniques, and remediation strategies. Knowledge of Qatar National Information Assurance (NIA) is a plus.
 </li>
 </ul>
</div>

Application Security

<div>
 
 Job Description
 
 
 
 Must Have
 
 
 <ul>
 <li>
 4+ years of software or machine-learning engineering experience, including hands-on experience deploying models to production.
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 Demonstrated track record operationalizing models developed by data scientists into reliable, scalable, and observable production systems.
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 <li>
 Production experience with classical machine-learning workloads &mdash; distinct from generative-AI application development.
 </li>
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 Strong collaboration skills across data science, software engineering, and platform teams.
 </li>
 <li>
 Commitment to building reliable, well-engineered, and maintainable systems.
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Experience with feature stores and large-scale or streaming data pipelines.
 </li>
 <li>
 Knowledge of infrastructure-as-code (e.g., Terraform) and cloud cost optimization.
 </li>
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 Experience with workflow orchestration tools (e.g., Apache Airflow).
 </li>
 <li>
 Familiarity with model-serving frameworks and API design for inference.
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 <li>
 Light exposure to generative-AI or LLM deployment.
 </li>
 <li>
 Experience operating systems in a regulated or enterprise environment.
 </li>
 <li>
 Cloud or MLOps certifications.
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Build, deploy, and maintain production machine-learning pipelines, from data ingestion and feature processing through to model serving.
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 Operationalize models developed by data scientists, ensuring reliability, scalability, reproducibility, and performance.
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 Implement MLOps practices including CI/CD for machine learning, model versioning, automated retraining, and model registries.
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 Design and maintain feature pipelines and, where relevant, feature stores.
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 Set up monitoring and alerting for model performance, data drift, and system health, and respond to degradation.
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 Optimize inference latency, throughput, and resource cost for deployed models.
 </li>
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 Containerize and orchestrate machine-learning workloads using Docker and Kubernetes on Oracle Cloud Infrastructure (OCI).
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 Automate and harden data and model workflows to reduce manual intervention.
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 Collaborate with data scientists on model handoff, packaging, and success metrics.
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 Work with software engineers to integrate models into client-facing applications and services.
 </li>
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 Apply software-engineering best practices including testing, code review, and documentation to machine-learning systems.
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 <li>
 Troubleshoot and resolve production issues across the model-serving stack.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Software Engineering, or a related field; equivalent experience accepted.
 </li>
 <li>
 Strong Python engineering skills and production experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
 </li>
 <li>
 Hands-on experience with MLOps tooling (e.g., MLflow, Kubeflow) and CI/CD pipelines.
 </li>
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 Proficiency with containerization and orchestration (Docker, Kubernetes).
 </li>
 <li>
 Experience building and maintaining data pipelines and processing large datasets.
 </li>
 <li>
 Experience with cloud infrastructure, ideally Oracle Cloud Infrastructure (OCI) and OCI Data Science.
 </li>
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 Understanding of model monitoring, drift detection, and retraining strategies.
 </li>
 <li>
 Solid software-engineering fundamentals including testing and version control.
 </li>
 </ul>
</div>

ML Engineer

<div>
 
 Job Description
 
 
 
 Must Have
 
 
 <ul>
 <li>
 3&ndash;5 years of experience in data science, analytics, or a quantitative modeling role.
 </li>
 <li>
 Hands-on proficiency in Python (pandas, NumPy, scikit-learn) and SQL for building and evaluating models end to end.
 </li>
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 Demonstrated ability to design experiments, validate model performance, and guard against overfitting and data leakage.
 </li>
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 Clear, reproducible, and well-documented analytical practice.
 </li>
 <li>
 Ability to communicate findings clearly to both technical colleagues and business stakeholders.
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Experience with visualization tools such as matplotlib, seaborn, Power BI, or Tableau.
 </li>
 <li>
 Exposure to cloud analytics environments, ideally Oracle Cloud Infrastructure (OCI).
 </li>
 <li>
 Experience with time-series analysis or natural-language data.
 </li>
 <li>
 Familiarity with collaborative workflows and code review.
 </li>
 <li>
 Exposure to working with engineering teams on model handoff.
 </li>
 <li>
 Data science certifications.
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Conduct exploratory data analysis to uncover patterns, relationships, and opportunities in client and operational data.
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 <li>
 Build, train, and evaluate predictive and descriptive models using Python (pandas, scikit-learn) and SQL.
 </li>
 <li>
 Build and evaluate deep learning models, such as neural networks, using frameworks like TensorFlow or PyTorch where they suit the problem.
 </li>
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 Perform feature engineering, data cleaning, and dataset preparation for modeling.
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 Design and analyze experiments, including A/B tests, to measure the impact of changes and interventions.
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 Prototype analytical solutions and iterate on them based on stakeholder and senior data scientist feedback.
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 Create clear visualizations, dashboards, and summaries that translate analysis into business insight.
 </li>
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 Validate model performance using appropriate metrics and guard against overfitting and data leakage.
 </li>
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 Document methodology, assumptions, and results to ensure reproducibility and knowledge sharing.
 </li>
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 Collaborate with senior data scientists, engineers, and analysts on larger initiatives.
 </li>
 <li>
 Support the preparation of analytical reports and presentations for clients and internal teams.
 </li>
 <li>
 Maintain and improve existing analytical code and notebooks
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Data Science) or equivalent experience; Master's an advantage.
 </li>
 <li>
 Proficiency in Python for data analysis (pandas, NumPy, scikit-learn) and working knowledge of SQL.
 </li>
 <li>
 Solid grounding in statistics and core machine-learning techniques (regression, classification, clustering).
 </li>
 <li>
 Working knowledge of deep learning concepts and frameworks such as TensorFlow or PyTorch.
 </li>
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 Experience with feature engineering and preparing real-world, messy datasets for modeling.
 </li>
 <li>
 Ability to communicate findings clearly to technical and business audiences.
 </li>
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 Understanding of model evaluation metrics and validation techniques.
 </li>
 <li>
 Familiarity with version control (Git) and reproducible analysis practices.
 </li>
 </ul>
</div>

Data Scientist

<div>
 
 Job Description
 
 
 
 Must Have
 
 
 <ul>
 <li>
 3&ndash;5 years of software engineering experience, including hands-on experience building LLM or generative-AI features.
 </li>
 <li>
 Production experience with RAG pipelines, embeddings, and vector databases.
 </li>
 <li>
 Demonstrated ability to design, test, and refine prompts and orchestration logic for LLM-driven workflows.
 </li>
 <li>
 Focus on the generative-AI application layer &mdash; distinct from classical model training and MLOps.
 </li>
 <li>
 Enthusiasm for working with fast-moving generative-AI technologies.
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Exposure to OCI Generative AI services or other cloud AI platforms.
 </li>
 <li>
 Familiarity with agent frameworks and tool integration.
 </li>
 <li>
 Experience deploying applications to the cloud, ideally Oracle Cloud Infrastructure (OCI).
 </li>
 <li>
 Awareness of responsible-AI and safety considerations.
 </li>
 <li>
 Experience with vector database tuning and retrieval optimization.
 </li>
 <li>
 AI or cloud certifications.
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Develop generative-AI features and applications using large language models and foundation-model APIs.
 </li>
 <li>
 Implement retrieval-augmented generation (RAG) pipelines, including document processing, embeddings, and vector search.
 </li>
 <li>
 Design, test, and refine prompts and orchestration logic for LLM-driven workflows.
 </li>
 <li>
 Build and integrate agentic components, tool-calling, and multi-step flows.
 </li>
 <li>
 Integrate AI capabilities into applications and services, including OCI Generative AI services.
 </li>
 <li>
 Evaluate model outputs against quality criteria and implement guardrails and validation checks.
 </li>
 <li>
 Build evaluation sets and run experiments to compare prompts, models, and configurations.
 </li>
 <li>
 Collaborate with senior AI engineers and product teams to deliver working AI features.
 </li>
 <li>
 Iterate on solutions based on evaluation results, performance, and user feedback.
 </li>
 <li>
 Document AI components, prompts, and integration patterns for maintainability.
 </li>
 <li>
 Contribute to internal reusable components and accelerators for generative-AI delivery.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field; equivalent experience accepted.
 </li>
 <li>
 Proficiency in Python and experience with LLM frameworks (e.g., LangChain, LlamaIndex) and foundation-model APIs.
 </li>
 <li>
 Working knowledge of RAG, embeddings, and vector databases.
 </li>
 <li>
 Understanding of prompt engineering and orchestration techniques.
 </li>
 <li>
 Ability to evaluate and improve the quality and reliability of AI outputs.
 </li>
 <li>
 Solid general software-engineering skills, including version control and testing.
 </li>
 <li>
 Experience integrating APIs and building application features.
 </li>
 </ul>
</div>

AI Engineer

<div>
 
 Job Description
 
 
 
 Must Have
 
 
 <ul>
 <li>
 5+ years of delivery experience, of which 4+ as Scrum Master / Squad Lead
 </li>
 <li>
 Proven track record leading mixed squads (frontend + backend + data + QA) to release in a government context.
 </li>
 <li>
 Demonstrated ability to enforce Definition of Done, code quality, and CI/CD discipline.
 </li>
 <li>
 Experience managing cross-squad dependencies on multi-squad programs (2+ squads in parallel).
 </li>
 <li>
 Bilingual capability &mdash; Arabic and English &mdash; for stakeholder workshops, documentation, and executive communication.
 </li>
 <li>
 Strong incident, risk, and escalation management instincts.
 
 
 </li>
 </ul>
 Nice to Have
 
 
 <ul>
 <li>
 Professional certification: PSM II/III, CSP-SM, PMI-ACP, or equivalent.
 </li>
 <li>
 Exposure to AI/ML delivery patterns, MLOps, or GenAI use cases.
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Lead a cross-functional delivery squad (BA, full-stack, front-end, QA, data engineer, data modeler) end-to-end across the sprint lifecycle.
 </li>
 <li>
 Facilitate all Scrum ceremonies: sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives.
 </li>
 <li>
 Partner with the Product Owner to maintain a healthy, well-prioritized backlog and ensure stories are ready before sprint commitment.
 </li>
 <li>
 Track sprint progress, burndown, velocity, and squad health; surface impediments early and drive their removal.
 </li>
 <li>
 Enforce Definition of Done; ensure all stories meet acceptance criteria, code review, and quality gates before release.
 </li>
 <li>
 Coordinate cross-squad dependencies with other Squad Leads, Solution Architect, and Program Manager.
 </li>
 <li>
 Manage RAID at squad level: log risks, issues, assumptions, and dependencies; escalate to Program Management per governance.
 </li>
 <li>
 Coach the squad in agile practices, technical excellence, continuous improvement, and self-management.
 </li>
 <li>
 Maintain transparent reporting to Program Manager and stakeholders on squad progress, blockers, and forecast.
 </li>
 <li>
 Champion engineering standards: code quality, CI/CD discipline, automated testing, peer review.
 </li>
 <li>
 Support release planning, deployment coordination, hypercare, and post-release retrospectives.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor degree in Computer Science, Software Engineering, Information Systems, or a related field.
 </li>
 <li>
 Strong working knowledge of Scrum, Kanban, and scaled agile frameworks (SAFe, LeSS, or equivalent).
 </li>
 <li>
 Hands-on proficiency with agile delivery tooling: Jira, Confluence, Azure DevOps, Miro / Mural.
 </li>
 <li>
 Sound technical background &mdash; able to challenge engineering decisions, read code at a high level, and interpret architecture artifacts.
 </li>
 <li>
 Demonstrated experience facilitating ceremonies, coaching teams, and resolving impediments across business and technical stakeholders.
 </li>
 <li>
 Excellent facilitation, written, and verbal communication skills in both Arabic and English.
 </li>
 </ul>
</div>

Scrum Lead