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mlops engineer

Finaira

<div>
 The MLOps Engineer II will independently design, implement, and manage the automation and streamlining of the machine learning lifecycle, ensuring reliable, efficient, and scalable deployment and monitoring of models. This role requires a solid understanding of DevOps practices, cloud technologies, and machine learning frameworks, with the ability to bridge the gap between data science and operations effectively.
 
 
 
 Focus
 
 : Independent automation of ML pipelines, deployment strategies, and monitoring in production.
 
 
 
 The difference you will make:
 
 
 
 <ul>
 <li>
 Independently design and implement CI/CD pipelines for machine learning models
 </li>
 <li>
 Automate the process of model training, validation, testing, and deployment using relevant tools
 </li>
 <li>
 Develop and maintain automated testing frameworks for machine learning models
 </li>
 <li>
 Develop and manage various model deployment strategies (e.g., A/B testing, canary deployments)
 </li>
 <li>
 Build and maintain scalable and reliable infrastructure for model serving (e.g., using Kubernetes, serverless functions)
 </li>
 <li>
 Implement and manage model versioning and rollback mechanisms
 </li>
 <li>
 Optimize model serving for latency, throughput, and resource utilization
 </li>
 <li>
 Implement and manage comprehensive monitoring and logging systems to track model performance and identify issues (e.g., model drift, data drift)
 </li>
 <li>
 Set up and manage alerting systems to notify the team of performance degradation
 </li>
 <li>
 Contribute to the development and implementation of model governance policies and procedures
 </li>
 <li>
 Ensure compliance with security and privacy requirements
 </li>
 <li>
 Collaborate effectively with data scientists to understand model requirements and dependencies
 </li>
 <li>
 Work with software engineers to integrate machine learning models into applications and services
 </li>
 <li>
 Develop and maintain APIs and interfaces for model access
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 Education: Bachelor's degree in computer science, Engineering, or a related field
 </li>
 <li>
 Experience: 2+ years of experience in DevOps, software engineering, or a related role with a focus on machine learning deployment and operations. Proven experience with cloud platforms and containerization technologies. Experience in the financial services industry is a plus
 </li>
 <li>
 Technical Skills:
 </li>
 <li>
 Strong proficiency in DevOps practices and tools (e.g., Jenkins, GitLab CI, Docker, Kubernetes)
 </li>
 <li>
 Solid knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and their machine learning services
 </li>
 <li>
 Knowledge of MLflow or Kubeflow for model management, or similar
 </li>
 <li>
 Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn)
 </li>
 <li>
 Strong experience with scripting and automation (e.g., Python, Bash)
 </li>
 <li>
 Solid understanding of software engineering principles and best practices
 </li>
 <li>
 Experience with infrastructure as code (IaC) tools (e.g., Terraform, CloudFormation)
 
 
 </li>
 </ul>
 
 Soft Skills:
 
 
 
 <ul>
 <li>
 Strong problem-solving and analytical skills with the ability to work independently
 </li>
 <li>
 Ability to work in a fast-paced and dynamic environment
 </li>
 <li>
 Automation mindset and a drive to improve efficiency
 </li>
 <li>
 Excellent collaboration and communication skills
 </li>
 <li>
 Customer-centric approach to solution development
 </li>
 <li>
 Strong team player
 </li>
 <li>
 Disciplined work ethic
 </li>
 <li>
 Good command of English language, both verbal and written
 </li>
 <li>
 Self-learner with a positive attitude
 
 
 </li>
 </ul>
 Finaira is an Equal Opportunity Employer and is committed to providing a workplace free of discrimination and harassment. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by the laws or regulations in the locations where we operate.
</div>

Mlops Engineer II

Intellias

<div>
 
 Dear Candidates,
 
 
 
 
 
 We are hiring for a Senior MLOps Engineer in Egypt.
 
 
 
 
 
 Exp: 5+ years
 
 
 Location: Egypt
 
 
 Notice Period: Any
 
 
 
 
 
 
 Skills Required: MLOps, ML, AI,
 
 
 
 
 
 
 
 Role:
 
 
 
 
 
 <ul>
 <li>
 5+ years of professional experience in MLOps, Machine learning engineering, or a similar role.
 </li>
 <li>
 Strong experience in Computer Vision, NLP, Generative AI demonstrated through relevant projects or research.
 </li>
 <li>
 Strong communication and interpersonal skills to collaborate effectively with cross-functional teams.
 </li>
 <li>
 Strong programming skills in languages such as Python or Rust.
 </li>
 <li>
 Experience in designing and implementing scalable machine learning pipelines for model training, validation, and deployment.
 </li>
 <li>
 Experience with cloud AI services (AWS / Azure / GCP)
 </li>
 <li>
 Experience managing and deploying model registry and feature store technologies.
 </li>
 <li>
 Experience with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.).
 </li>
 <li>
 Experience working with containerization technologies such as Docker and Kubernetes.
 </li>
 <li>
 Experience putting Deep Learning models into production with frameworks such as Pytorch or Tensorflow.
 </li>
 <li>
 Experience developing REST APIs using frameworks such as FastAPI or similar.
 </li>
 <li>
 Experience developing GenAI agents using libraries such as LangChain or similar.
 </li>
 <li>
 Experience in evaluating LLM agents, LLM observability, and agent memory management.
 </li>
 <li>
 Experience with infrastructure-as-code (IaC) tools such as Terraform.
 </li>
 <li>
 Master or higher degree in Computer Science, Engineering, or a related field.
 </li>
 </ul>
 
 
 
 
 
 Nice to have:
 
 
 <ul>
 <li>
 Experience of deploying LLM models on premise
 </li>
 <li>
 GCP Professional Machine Learning Engineer certification (not older than a year)
 </li>
 <li>
 Experience with data science projects
 </li>
 <li>
 Industry knowledge in retail, manufacturing, healthcare, energy, finance or telecom
 </li>
 </ul>
 
 
 
 
 
 Responsibilities:
 
 
 
 
 
 <ul>
 <li>
 Execution and guidance of technical POCs and projects
 </li>
 <li>
 Consulting, discovery phases and suggestions for executing the MLOps projects by following best practices and latest advancements in the field
 </li>
 <li>
 Cooperating on presales and upsales with rest of the team
 </li>
 <li>
 Participating in public speaking events
 </li>
 </ul>
 
 
 
 
 If you find above job description relevant to your profile the do DM me or forward me your resume on Ankita.Soni@intellias.com
 
 
 
 
 
 Regards,
 
 
 Ankita
 
 
 Ankita.Soni@intellias.com
 
</div>

Senior Machine Learning Engineer

Capgemini

<div>
 
 About Capgemini
 
 
 
 Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of &euro;22.1 billion.
 
 
 
 Job Responsibities
 
 
 
 
 Machine Learning &amp; AI Solutions
 
 
 
 <ul>
 <li>
 Design and implement Agentic AI solutions and applications.
 </li>
 <li>
 Experience in Data Science, Data Mining, Machine Learning tasks (e.g. feature engineering, model training, evaluation, and deployment).
 </li>
 <li>
 Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.
 
 
 </li>
 </ul>
 
 Cloud &amp; Data Engineering
 
 
 
 <ul>
 <li>
 Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.
 
 
 </li>
 </ul>
 
 MLOps &amp; Automation
 
 
 
 <ul>
 <li>
 Familiarity with CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.
 
 
 </li>
 </ul>
 
 Collaboration &amp; Innovation
 
 
 
 <ul>
 <li>
 Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.
 </li>
 <li>
 Contribute to knowledge sharing and best practices across the team.
 
 
 </li>
 </ul>
 
 Job Requirements
 
 
 
 
 Technical Skills:
 
 
 
 <ul>
 <li>
 Strong proficiency in python, data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and agentic application development libraries (e.g. LangChain, LangGraph).
 </li>
 <li>
 Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).
 </li>
 <li>
 Familiarity with cloud platforms (Azure, AWS, GCP) and their ML services.
 </li>
 <li>
 Familiarity with MLOps practices, including CI/CD, containerization (Docker), and orchestration (Kubernetes).
 </li>
 <li>
 Familiarity with Git and collaborative development workflows.
 
 
 </li>
 </ul>
 
 Experience
 
 
 
 <ul>
 <li>
 2-5 years of experience in solving real-world business problems using AI
 </li>
 <li>
 Proven track record of deploying and maintaining ML models in production environments.
 
 
 </li>
 </ul>
 
 Communication Skills
 
 
 
 <ul>
 <li>
 Excellent written and verbal communication skills to collaborate effectively with cross-functional teams.
 </li>
 <li>
 Fluent in Arabic &amp; English, both spoken and written (German is a plus!)
 </li>
 <li>
 Able to listen to and understand customer needs and articulate them as technical requirements to the team.
 
 
 </li>
 </ul>
 
 Additional Skills
 
 
 
 <ul>
 <li>
 Strong problem-solving abilities and attention to detail.
 </li>
 <li>
 Ability to work collaboratively in a team environment.
 </li>
 <li>
 Self-motivated with a continuous learning mindset.
 </li>
 </ul>
</div>

Data Scientist (Machine Learning Engineer)

Quality Standards for Information Technology - QSIT

<div>
 
 
 Job Responsibilities:
 
 
 <ul>
 <li>
 Design, develop, and optimize AI/ML solutions for real business use cases.
 </li>
 <li>
 Build and evaluate machine learning, deep learning, and generative AI models.
 </li>
 <li>
 Analyze and preprocess structured and unstructured data to support model development and solution quality.
 </li>
 <li>
 Validate model performance, reliability, scalability, and production readiness.
 </li>
 <li>
 Ensure AI solutions align with enterprise standards for maintainability, quality, and governance.
 </li>
 <li>
 Design and implement LLM-based and Generative AI solutions, including RAG pipelines, prompt engineering, retrieval optimization, and evaluation.
 </li>
 <li>
 Work with embeddings, vector search, reranking, and enterprise knowledge integration patterns.
 </li>
 <li>
 Support use cases such as document intelligence, AI assistants, chatbots, search augmentation, and decision-support solutions.
 </li>
 <li>
 Own AI solution delivery from requirement analysis through deployment, integration, and handover.
 </li>
 <li>
 Collaborate with software, data, and platform teams to integrate AI capabilities into enterprise platforms and APIs.
 </li>
 <li>
 Support monitoring, optimization, and post-go-live improvement of AI services.
 </li>
 <li>
 Apply MLOps / LLMOps practices for reproducibility, deployment automation, monitoring, and lifecycle management.
 </li>
 <li>
 Support technical proposal development, bid responses, and client-facing AI solution discussions when required.
 </li>
 <li>
 Contribute to technical presentations, solution write-ups, and AI capability positioning.
 </li>
 <li>
 Provide technical insight during opportunity shaping and feasibility assessments.
 </li>
 </ul>
 
 
 
 
 
 Required Qualifications:
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in Computer Science, AI, Data Science, Computer Engineering, or related field.
 </li>
 <li>
 From 4&ndash;6 years of hands-on experience delivering end-to-end AI/ML solutions in real business environments (not just PoCs/research).
 </li>
 <li>
 Strong Python skills; hands-on with PyTorch, TensorFlow, and Scikit-learn.
 </li>
 <li>
 Experience in data preprocessing, feature engineering, model evaluation, and optimization.
 </li>
 <li>
 Practical experience with Generative AI, LLMs, RAG, embeddings, and vector databases.
 </li>
 <li>
 Familiar with prompt engineering, LLM evaluation, guardrails, and inference optimization.
 </li>
 <li>
 Experience with MLOps/LLMOps (deployment, monitoring, versioning, lifecycle).
 </li>
 <li>
 Cloud experience: Azure AI / Azure OpenAI preferred; AWS/GCP a plus.
 </li>
 <li>
 Experience with APIs, microservices, enterprise integration, and Docker/containers.
 </li>
 <li>
 Experience in domains like NLP, document intelligence, CV, forecasting, recommendations, or conversational AI.
 </li>
 <li>
 Strong analytical, communication, and cross-functional collaboration skills; leadership/mentoring mindset.
 </li>
 <li>
 Proven delivery of deployed AI/ML solutions.
 </li>
 <li>
 Provide project examples with: domain/client, AI approach, role, and deployment environment.
 </li>
 <li>
 Show involvement in deployment, handover, and post-go-live support/optimization.
 </li>
 </ul>
 
 
</div>

Senior AI Engineer

<div>
 
 About Capgemini
 
 
 
 Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of &euro;22.1 billion.
 
 
 
 Job Description
 
 
 
 <ul>
 <li>
 Design and implement Agentic AI solutions and applications.
 </li>
 <li>
 Lead the end-to-end lifecycle of machine learning and data science delivery engagements, from data ingestion and feature engineering to model training, evaluation, and deployment.
 </li>
 <li>
 Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.
 </li>
 <li>
 Architect and optimize scalable data pipelines and cloud-based infrastructures (Azure, AWS, GCP) to support ML workloads.
 </li>
 <li>
 Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.
 </li>
 <li>
 Develop and maintain CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.
 </li>
 <li>
 Integrate monitoring and alerting systems to track model drift, data quality, and system performance.
 </li>
 <li>
 Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.
 </li>
 <li>
 Mentor junior engineers and contribute to knowledge sharing and best practices across the team.
 
 
 </li>
 </ul>
 
 Job Requirements
 
 
 
 <ul>
 <li>
 6-10 years of experience working with complex datasets and solving real-world business problems using AI
 </li>
 <li>
 Proven track record of deploying and maintaining ML models in production environments and delivering AI solution architecture.
 </li>
 <li>
 Strong proficiency in Python, data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and agentic application development libraries (e.g. LangChain, LangGraph).
 </li>
 <li>
 Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).
 </li>
 <li>
 Deep understanding of cloud platforms (Azure, AWS, GCP) and their ML services.
 </li>
 <li>
 Skilled in MLOps practices including CI/CD, containerization (e.g. Docker), and orchestration (Kubernetes).
 </li>
 <li>
 Strong command of Git and collaborative development workflows.
 </li>
 <li>
 Excellent written and verbal communication skills, capable of conveying complex concepts to a variety of audiences.
 </li>
 <li>
 Fluent in Arabic &amp; English, both spoken and written (German is a plus!)
 </li>
 <li>
 Able to listen to and understand customer needs and articulate them as technical requirements to the team.
 </li>
 <li>
 Strong problem-solving abilities and attention to detail.
 </li>
 <li>
 Strategic thinking with the ability to align data science initiatives with business objectives.
 </li>
 <li>
 Strong leadership and team management skills with the ability to inspire and motivate.
 </li>
 <li>
 Ability to work collaboratively in a team environment.
 </li>
 <li>
 Self-motivated with a strong growth mindset.
 </li>
 </ul>
</div>

Senior Data Scientist - AI & ML Engineer

700apps

<div>
 We are looking for a
 
 Senior AI Engineer
 
 to join our growing technology team and lead the development of advanced AI solutions. In this role, you will design, build, and deploy scalable AI models that power intelligent products and services.
 
 
 You will work closely with cross-functional teams to transform complex business challenges into innovative AI-driven solutions.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design end-to-end AI/ML system architecture from data ingestion to deployment and monitoring.
 </li>
 <li>
 Define scalable and reliable ML pipelines.
 </li>
 <li>
 Select the appropriate tools, frameworks, and infrastructure.
 </li>
 <li>
 Ensure performance, security, scalability, and maintainability of AI systems.
 </li>
 <li>
 Design APIs and AI services for integration with products and platforms
 </li>
 <li>
 Collaborate with engineering, product, and data teams to deliver AI-powered features.
 </li>
 <li>
 Develop, train, and fine-tune machine learning and deep learning models.
 </li>
 <li>
 Work on advanced AI solutions such as LLMs, NLP systems, or computer vision models.
 </li>
 <li>
 Optimize models for accuracy, speed, and cost efficiency.
 </li>
 <li>
 Conduct experiments and evaluate model performance using best practices.
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 7+ years of experience in AI / Machine Learning development.
 </li>
 <li>
 Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
 </li>
 <li>
 Experience with LLMs, NLP, or Generative AI is highly preferred.
 </li>
 <li>
 Experience with MLOps, model deployment, and cloud platforms (AWS, Azure, or GCP).
 </li>
 <li>
 Strong understanding of data structures, algorithms, and software engineering best practices.
 </li>
 <li>
 Excellent problem-solving and communication skills.
 </li>
 <li>
 Experience with cloud platforms such as AWS, GCP, or Azure.
 </li>
 <li>
 Experience with Docker and Kubernetes.
 </li>
 <li>
 Familiarity with MLOps tools such as MLflow, Airflow, or similar.
 </li>
 </ul>
</div>

<div>
 The Principal AI Engineer provides technical leadership and direction for a team of AI Engineer, overseeing the end-to-end development and deployment of significant AI initiatives. This role involves driving technical strategy, setting best practices, mentoring team members, and ensuring the delivery of high-quality, impactful AI solutions that align with business objectives.
 
 
 
 Focus:
 
 End-to-End Initiative Leadership, AI/ML Strategy &amp; Mentorship
 
 
 
 The difference you will make:
 
 
 
 <ul>
 <li>
 Provide technical leadership, guidance, and mentorship to a team of AI Engineer
 </li>
 <li>
 Architect, design, and oversee the implementation of complex AI/ML systems and platforms from conception to production
 </li>
 <li>
 Drive the technical roadmap for AI projects, translating business requirements into technical specifications and actionable plans
 </li>
 <li>
 Champion best practices in AI/ML engineering, MLOps, software development, code quality, and testing
 </li>
 <li>
 Lead technical discussions, architectural reviews, and problem-solving sessions within the team and across departments
 </li>
 <li>
 Collaborate closely with product management, data science, and other engineering teams to define project scope, technical feasibility, and resource allocation
 </li>
 <li>
 Identify and evaluate new technologies, tools, and methodologies to enhance the team's capabilities and efficiency
 </li>
 <li>
 Conduct performance evaluations and provide constructive feedback to team members, contributing to their professional growth
 </li>
 <li>
 Act as a primary technical point of contact for stakeholders regarding AI solutions
 </li>
 <li>
 Hands-on contribution to critical code components, architectural design, and complex problem-solving
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 
 What you will bring to the role:
 
 
 
 
 Education:
 
 
 
 <ul>
 <li>
 Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. Master's degree or Ph.D. highly preferred
 
 
 </li>
 </ul>
 
 Experience:
 
 
 
 <ul>
 <li>
 8+ years of extensive experience in AI/ML engineering, with at least 2-3 years in a technical leadership or lead engineer role, successfully leading significant AI projects from end-to-end
 
 
 </li>
 </ul>
 
 Technical Skills:
 
 
 
 <ul>
 <li>
 Expert proficiency in Python and deep expertise in multiple AI/ML frameworks (e.g., TensorFlow, PyTorch, Keras)
 </li>
 <li>
 Demonstrated experience in designing and implementing large-scale, production-grade AI systems, including distributed training and serving
 </li>
 <li>
 In-depth knowledge and practical experience with MLOps principles, tools, and platforms (e.g., Kubeflow, MLflow, Airflow, Docker, Kubernetes)
 </li>
 <li>
 Strong architectural skills for building robust, scalable, and maintainable AI infrastructures
 </li>
 <li>
 Proficiency in cloud environments (AWS, Azure, GCP) and their AI/ML services
 </li>
 <li>
 Excellent understanding of data engineering principles, data governance, and database technologies
 </li>
 <li>
 Ability to evaluate and make sound technical decisions on complex trade-offs
 
 
 </li>
 </ul>
 
 Soft Skills:
 
 
 
 <ul>
 <li>
 Exceptional leadership, communication, and presentation skills, with the ability to influence and persuade stakeholders at all levels, including executive leadership
 </li>
 <li>
 Proven ability to think strategically and translate complex business challenges into innovative and impactful data science initiatives
 </li>
 <li>
 Strong mentoring and coaching skills, with a passion for developing the talents of junior team members
 </li>
 <li>
 Excellent collaboration and negotiation skills, with the ability to work effectively across diverse, cross-functional teams and build strong relationships
 
 
 </li>
 </ul>
 Finaira is an Equal Opportunity Employer and is committed to providing a workplace free of discrimination and harassment. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by the laws or regulations in the locations where we operate.
</div>

Principal AI Engineer

Explore 174+ Mlops engineer in Cairo this April. Find roles across top companies and apply now to boost your career.

174 mlops engineer jobs In Cairo

Mlops Engineer II

Senior Machine Learning Engineer

Data Scientist (Machine Learning Engineer)

Senior AI Engineer

Senior Data Scientist - AI & ML Engineer

Senior AI Engineer

Principal AI Engineer