lead data scientist - ai/ml (generative ai)

full stack engineer

senior software engineer Jobs in Cairo Egypt

senior software engineer

data engineer

junior/standard software engineer, power Jobs in Cairo Egypt

junior/standard software engineer, power

lead data scientist - ai/ml (generative ai) Jobs in Cairo Egypt

senior machine learning engineer

Unifonic

<div>
 
 Job Description
 
 
 
 Proudly voted a Great Place to Work&reg;, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate. Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.
 
 
 We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we&rsquo;re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!
 
 
 
 Meet the team!
 
 
 
 Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic&rsquo;s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
 
 
 As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection. This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.
 
 
 
 Help Us Shape The Future Of Communication By
 
 
 
 <ul>
 <li>
 Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.
 </li>
 <li>
 Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.
 </li>
 <li>
 Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.
 </li>
 <li>
 Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).
 </li>
 <li>
 Mentoring junior team members on the team, sharing knowledge, and advising the best machine learning and software engineering practices and approaches.
 </li>
 <li>
 Establishing and maintaining robust communication channels with other cross-functional teams to facilitate the integration of machine learning solutions into other Unifonic products.
 </li>
 <li>
 Developing and optimizing highly confident machine learning algorithms and models and creating/exposing the service APIs using frameworks such as Flask, FastAPIs, or other relevant frameworks.
 </li>
 <li>
 Staying up to date with the latest machine learning research papers, and AI trends (i.e. Generative AI).
 </li>
 <li>
 Collaborating with the data engineering team and other teams to collect and analyze extensive datasets, extracting insights and patterns, in real-time, near-real-time, or batch processing mode.
 </li>
 <li>
 Implementing proof of concepts and prototypes to demonstrate the potential of new AI use cases and innovations.
 </li>
 <li>
 Building scalable, maintainable machine learning services, which should handle thousands of requests per second, and help to perform the required load tests to meet the SLA.
 </li>
 <li>
 Reviewing the code of other team members and suggesting improvements to ensure the SOLID principles and clean architecture.
 </li>
 <li>
 Assisting in the project documentation and demos.
 
 
 </li>
 </ul>
 Job requirements
 
 
 
 What You'll Bring
 
 
 
 <ul>
 <li>
 Proven experience designing and implementing RAG systems, including familiarity with various retrieval strategies (e.g., BM25, dense retrieval, hybrid approaches) and knowledge graph integration.
 </li>
 <li>
 Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar tools for building and managing autonomous agents.
 </li>
 <li>
 Deep expertise in various NLP techniques and models, including but not limited to:
 
 
 </li>
 </ul>
 Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)
 
 
 Large Language Models (LLMs) and their fine-tuning/adaptation
 
 
 Vector embeddings and similarity search
 
 
 Text classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.
 
 
 <ul>
 <li>
 Hands-on 3-5 years of relevant work experience as a Machine Learning Engineer.
 </li>
 <li>
 Hands-on 3+ years of experience with Python.
 </li>
 <li>
 Excellent analytical abilities, with the capacity to collect, organize, and analyze large datasets to glean valuable insights.
 </li>
 <li>
 End-to-end experience in training, evaluating, testing, and deploying machine learning products in production.
 </li>
 <li>
 Ability to write world-class code in Python (SOLID principles), considering the best software engineering fundamentals, i.e. data structures, algorithms, and data modeling
 </li>
 <li>
 Solid experience in ML frameworks such as NumPy, Pandas, Scikit-Learn, PyTorch, Keras, BERT, Tensorflow, and similar.
 </li>
 <li>
 Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
 </li>
 <li>
 Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, and dimensionality reduction.
 </li>
 <li>
 Basic knowledge of Kubernetes and Docker is nice to have.
 </li>
 <li>
 Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
 </li>
 <li>
 Experience with the Hugging Face libraries (i.e. transformers).
 </li>
 <li>
 Experience fine-tuning pre-trained models and using vector search to enhance LLMs results.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques.
 </li>
 <li>
 Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
 </li>
 <li>
 Familiar with Agile methodologies i.e. scrum and kanban.
 </li>
 <li>
 Ability to develop high-level architecture and low-level design, End-to-end for a specific project.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is a plus.
 </li>
 <li>
 Experience with LLM frameworks (i.e. LangChain) and prompt engineering techniques is nice to have.
 </li>
 <li>
 Experience in event sourcing patterns and tools i.e. Kafka, RabbitMQ, or similar is nice to have.
 </li>
 <li>
 General knowledge of Data warehouse tools e.g. Vertica is a plus.
 </li>
 <li>
 A Bachelor&rsquo;s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software, etc).
 </li>
 <li>
 Excellent communication and collaboration skills.
 </li>
 <li>
 Good level of spoken and written Arabic and English.
 
 
 </li>
 </ul>
 
 As a Unifone you will receive a range of benefits:
 
 
 
 <ul>
 <li>
 Competitive salary and bonus.
 </li>
 <li>
 Unifonic share scheme (we are all owners!).
 </li>
 <li>
 30 holiday days after the first anniversary.
 </li>
 <li>
 Your Birthday off!
 </li>
 <li>
 Spend up to 25 days per year working from anywhere in the world!
 </li>
 <li>
 Paid leave for new parents.
 </li>
 <li>
 LinkedIn learning license.
 </li>
 </ul>
</div>

Senior Machine Learning Engineer

Siemens

<div>
 
 About Brightly
 
 
 
 Brightly is a global leader in intelligent, cloud‑based asset lifecycle management, empowering 12,000+ clients with predictive insights to optimize maintenance, energy, and capital planning&mdash;backed by Siemens. Our products help schools, hospitals, cities, and manufacturers keep critical infrastructure running efficiently and sustainably.
 
 
 
 The Opportunity
 
 
 
 We&rsquo;re looking for a Senior Machine Learning Engineer to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
 
 
 
 What You&rsquo;ll Do
 
 
 
 <ul>
 <li>
 Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
 </li>
 <li>
 Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts.
 </li>
 <li>
 Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
 </li>
 <li>
 Scale training &amp; inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
 </li>
 <li>
 MLOps &amp; governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
 </li>
 <li>
 Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability.
 </li>
 <li>
 Mentor &amp; lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
 </li>
 <li>
 Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues.
 
 
 </li>
 </ul>
 (Consistent with ML engineer responsibilities to analyze data before model development.)
 
 
 <ul>
 <li>
 Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Bring
 
 
 
 
 Required experience
 
 
 
 <ul>
 <li>
 5&ndash;7 years total software/ML engineering experience, with 3+ years building and operating ML systems in production.
 </li>
 <li>
 1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph.
 </li>
 <li>
 AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS). (Representative AWS ML role skills.)
 </li>
 <li>
 Modeling &amp; frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
 </li>
 <li>
 MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
 </li>
 <li>
 Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.
 
 
 </li>
 </ul>
 
 Nice to have
 
 
 
 <ul>
 <li>
 Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
 </li>
 <li>
 Exposure to sustainability/asset/intelligent operations domains.
 </li>
 <li>
 Familiarity with security &amp; compliance for ML systems in enterprise environments. (Frequently included in senior ML roles.)
 
 
 </li>
 </ul>
 How you&rsquo;ll work
 
 
 <ul>
 <li>
 Pragmatic and product‑oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
 </li>
 <li>
 Engineering excellence: You write production‑quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
 </li>
 <li>
 Collaborative leadership: You mentor peers and influence architecture across teams. (Industry‑standard senior expectations.)
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s in CS/EE/Math or related field (Master&rsquo;s preferred) or equivalent practical experience. (Typical for senior ML roles.)
 
 
 </li>
 </ul>
 Join a mission‑driven team building technology that keeps communities running&mdash;safer, greener, and more resilient&mdash;at global scale. You&rsquo;ll pair startup‑speed product work with the reach and rigor of Siemens.
 
 
 
 The Brightly culture
 
 
 
 We&rsquo;re guided by a vision of community that serves the ambitions and wellbeing of all people, and our professional communities are no exception. We model that ideal every day by being supportive, collaborative partners to one another, conscientiously making space for our colleagues to grow and thrive. Our passionate team is driven to create a future where smarter infrastructure protects the environments that shape and connect us all. That brighter future starts with us.
</div>

Arrow Electronics

<div>
 
 Position
 
 
 
 Data Scientist / Senior Machine Learning Engineer
 
 
 
 Job Description
 
 
 
 
 Position Overview:
 
 
 
 As Data Scientist / Senior Machine Learning Engineer at Arrow Electronics, you will assume a pivotal role in driving our data science and machine learning initiatives to reach unprecedented heights. Collaborating closely with cross-functional multinational teams, your responsibilities will encompass the end-to-end machine learning lifecycle, including stakeholder communications, data analysis, designing, development, deployment, and optimization of cutting-edge models aimed at addressing intricate challenges and generating actionable insights. Your expertise in data analysis, model development, and deployment will be crucial in shaping the future of our company.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Machine Learning Model Development: Design, develop, and implement advanced machine learning models and algorithms to solve real-world problems in various domains.
 </li>
 <li>
 Data Analysis: Explore and analyze large datasets to extract meaningful insights and identify trends, anomalies, and patterns.
 </li>
 <li>
 Model Evaluation: Evaluate the performance of machine learning models using appropriate metrics and fine-tune models for optimal results.
 </li>
 <li>
 Deployment: Lead the deployment of machine learning models into production environments and ensure scalability and efficiency.
 </li>
 <li>
 Cross-functional Collaboration: Collaborate closely with business stakeholders, software engineers, data engineers, and domain experts to integrate machine learning solutions into our products and services.
 </li>
 <li>
 Mentorship: Provide guidance and mentorship to junior data scientists and machine learning engineers, fostering a culture of continuous learning and growth.
 </li>
 <li>
 Research and Innovation: Stay up-to-date with the latest advancements in the field of data science and machine learning and apply innovative techniques to solve complex challenges.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Proven experience (2 to 5 years) in developing and deploying machine learning models in a production environment.
 </li>
 <li>
 Proficiency in programming languages, especially Python, and experience with data-related libraries such as pandas, matplotlib, NumPy, etc.
 </li>
 <li>
 Strong knowledge of machine learning frameworks and libraries.
 </li>
 <li>
 Expertise in data preprocessing, feature engineering, and model selection.
 </li>
 <li>
 Familiarity with large language models and AI agent design.
 </li>
 <li>
 Experience with text-based NLP, information extraction, entity recognition, and text-related data science tasks is highly desirable.
 </li>
 <li>
 Strong communication skills and the ability to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience with vectorized databases and unstructured search engines is a plus.
 </li>
 <li>
 A Master&rsquo;s degree in computer science, Statistics, Data Science, or a related field is a plus.
 </li>
 <li>
 Publications or contributions to open-source projects are a plus.
 
 
 </li>
 </ul>
 If you are a passionate and experienced Data Scientist / Senior Machine Learning Engineer who thrives on solving challenging problems and wants to make a significant impact in the field, we encourage you to apply and join our dynamic team at Arrow Electronics Together, we will drive innovation and shape the future of data science and machine learning.
 
 
 
 Location:
 
 
 
 EG-Cairo, Egypt (Al Emdad &amp; Al Tamween)
 
 
 
 Time Type
 
 
 
 Full time
 
 
 
 Job Category
 
 
 
 Information Technology
</div>

Data Scientist / Senior Machine Learning Engineer

Finaira

<div>
 The Senior AI Engineer is a senior individual contributor with deep expertise in a specific domain of AI/ML, responsible for tackling the most complex technical challenges, driving architectural decisions for critical AI components, and setting technical standards. This role requires a blend of advanced research understanding and robust engineering skills to bring cutting-edge AI innovations into production.
 
 
 
 The difference you will make
 
 
 
 <ul>
 <li>
 Act as a subject matter expert in one or more specialized AI/ML domains (e.g., Generative AI, Explainable AI, Large Language Models, Reinforcement Learning, Real-time Anomaly Detection).
 </li>
 <li>
 Lead the design and implementation of highly scalable, fault-tolerant, and high-performance AI systems and infrastructure.
 </li>
 <li>
 Evaluate and integrate new AI/ML research, algorithms, and technologies into product development.
 </li>
 <li>
 Drive continuous improvement in MLOps, model monitoring, and model governance frameworks.
 </li>
 <li>
 Provide expert technical guidance and mentorship to other AI Engineer 1s and Associates.
 </li>
 <li>
 Conduct complex performance tuning, optimization, and debugging of AI/ML models and pipelines.
 </li>
 <li>
 Participate in strategic planning for AI initiatives, identifying opportunities for technological advancement.
 </li>
 <li>
 Influence technical direction and architecture discussions within the AI/ML team and across engineering.
 </li>
 <li>
 Lead code reviews for critical components, ensuring architectural integrity, performance, and maintainability
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 Education:
 
 
 Bachelor's degree or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related quantitative field.
 
 
 Experience:
 
 
 <ul>
 <li>
 5+ years of progressive experience in AI/ML engineering, with a proven track record of designing and deploying complex AI systems into production.
 </li>
 <li>
 Demonstrated deep expertise in a specific AI sub-field
 
 
 </li>
 </ul>
 Technical Skills:
 
 
 <ul>
 <li>
 Expert-level proficiency in Python and advanced ML/DL frameworks (e.g., PyTorch, TensorFlow, JAX).
 </li>
 <li>
 Extensive experience with cloud-native AI/ML services and distributed computing frameworks (e.g., Spark, Ray).
 </li>
 <li>
 Deep understanding and hands-on experience with MLOps best practices, including CI/CD, model serving, and monitoring in production environments.
 </li>
 <li>
 Strong architectural design skills for complex data pipelines and scalable AI inference systems.
 </li>
 <li>
 Proficiency in system design, distributed systems, and performance optimization.
 </li>
 <li>
 Ability to critically evaluate and apply academic research to real-world problems
 
 
 </li>
 </ul>
 Soft Skills:
 
 
 <ul>
 <li>
 Strong ownership and accountability for assigned work.
 </li>
 <li>
 Clear and professional verbal and written communication.
 </li>
 <li>
 Effective time management and task prioritization. o Analytical thinking and problem-solving skills.
 </li>
 <li>
 Ability to work independently, collaborate effectively and mentor junior team members.
 </li>
 <li>
 Ability to actively listen to customers and accurately understand their needs, expectations, and challenges.
 </li>
 <li>
 Adaptability and learning agility in a fast-paced environment.
 </li>
 <li>
 Strong attention to detail and a commitment to quality.
 </li>
 <li>
 Receptiveness to feedback and continuous improvement mindset
 </li>
 </ul>
</div>

Senior AI Engineer

<div>
 
 The Senior AI Engineer is a senior individual contributor with deep expertise in a specific domain of AI/ML, responsible for tackling the most complex technical challenges, driving architectural decisions for critical AI components, and setting technical standards. This role requires a blend of advanced research understanding and robust engineering skills to bring cutting-edge AI innovations into production.
 
 
 
 
 
 The difference you will make:
 
 <ul>
 <li>
 Act as a subject matter expert in one or more specialized AI/ML domains (e.g., Generative AI, Explainable AI, Large Language Models, Reinforcement Learning, Real-time Anomaly Detection).
 </li>
 <li>
 Lead the design and implementation of highly scalable, fault-tolerant, and high-performance AI systems and infrastructure.
 </li>
 <li>
 Evaluate and integrate new AI/ML research, algorithms, and technologies into product development.
 </li>
 <li>
 Drive continuous improvement in MLOps, model monitoring, and model governance frameworks.
 </li>
 <li>
 Provide expert technical guidance and mentorship to other AI Engineer 1s and Associates. &bull; Conduct complex performance tuning, optimization, and debugging of AI/ML models and pipelines.
 </li>
 <li>
 Participate in strategic planning for AI initiatives, identifying opportunities for technological advancement.
 </li>
 <li>
 Influence technical direction and architecture discussions within the AI/ML team and across engineering.
 </li>
 <li>
 Lead code reviews for critical components, ensuring architectural integrity, performance, and maintainability.
 </li>
 </ul>
 
 
 
 
 What you will bring to the role:
 
 
 
 Education:
 
 Bachelor&rsquo;s degree or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a closely related quantitative field.
 
 
 
 
 
 
 Experience:
 
 
 <ul>
 <li>
 5+ years of progressive experience in AI/ML engineering, with a proven track record of designing and deploying complex AI systems into production. Demonstrated deep expertise in a specific AI sub-field.
 </li>
 </ul>
 
 
 Technical Skills:
 
 
 <ul>
 <li>
 Expert-level proficiency in Python and advanced ML/DL frameworks (e.g., PyTorch, TensorFlow, JAX).
 </li>
 <li>
 Extensive experience with cloud-native AI/ML services and distributed computing frameworks (e.g., Spark, Ray).
 </li>
 <li>
 Deep understanding and hands-on experience with MLOps best practices, including CI/CD, model serving, and monitoring in production environments.
 </li>
 <li>
 Strong architectural design skills for complex data pipelines and scalable AI inference systems.
 </li>
 <li>
 Proficiency in system design, distributed systems, and performance optimization. o Ability to critically evaluate and apply academic research to real-world problems.
 </li>
 </ul>
 
 
 Soft Skills:
 
 
 <ul>
 <li>
 Strong ownership and accountability for assigned work.
 </li>
 <li>
 Clear and professional verbal and written communication.
 </li>
 <li>
 Effective time management and task prioritization.
 </li>
 <li>
 Analytical thinking and problem-solving skills.
 </li>
 <li>
 Ability to work independently, collaborate effectively and mentor junior team members.
 </li>
 <li>
 Ability to actively listen to customers and accurately understand their needs, expectations, and challenges.
 </li>
 <li>
 Adaptability and learning agility in a fast-paced environment.
 </li>
 <li>
 Strong attention to detail and a commitment to quality.
 </li>
 <li>
 Receptiveness to feedback and continuous improvement mindset
 </li>
 </ul>
 
 
 
</div>

<div>
 Siemens Digital Industries (DI) is a global leader in automation and digitalization. In close collaboration with partners and customers, we drive digital transformation across process and discrete industries. Our Digital Enterprise portfolio delivers an end-to-end ecosystem of products, solutions, and services that help organizations of all sizes integrate and digitalize their entire value chain.
 
 
 Our industry-focused portfolio enables customers to achieve greater efficiency, flexibility, and innovation. We continuously evolve our technology stack by integrating groundbreaking future technologies. Siemens Digital Industries Software is headquartered in Nuremberg, Germany, and employs approximately 75,000 people worldwide.
 
 
 We offer a role that combines responsibility, autonomy, and the opportunity to contribute proactively. Our culture promotes teamwork, ownership, and continuous individual development.
 
 
 Learn more: https://siemensneo.com/disw/
 
 
 We're looking for a
 
 Principal Machine Learning Engineer
 
 to lead LLM‑powered application development&mdash;from prototype to production&mdash;on AWS. You&rsquo;ll design robust ML/LLM services that power search, recommendations, copilots, and workflow automation in Brightly&rsquo;s platform, partnering closely with product, data, and engineering teams. Responsibilities and skill expectations reflect current industry practice for senior ML/LLM engineers, including end‑to‑end model lifecycle ownership, production‑grade code, and MLOps.
 
 
 
 Responsibilities:
 
 
 
 <ul>
 <li>
 Build LLM applications: Design and deliver RAG pipelines, prompt workflows, agent tools, safety guardrails, and evaluation frameworks &mdash; optimizing for performance, cost, and quality.
 </li>
 <li>
 Own the ML lifecycle end-to-end: From data prep and feature engineering to fine-tuning (LoRA/QLoRA), testing, deployment, monitoring, and continuous improvement.
 </li>
 <li>
 Productionize on AWS: Launch scalable ML services using AWS infrastructure and ML tooling, with strong observability and cost efficiency.
 </li>
 <li>
 Scale training &amp; inference: Apply distributed training, quantization, caching, and vector databases to improve performance and efficiency.
 </li>
 <li>
 Drive MLOps &amp; governance: Build CI/CD pipelines for ML, manage model/version registries, track experiments, and enforce responsible AI practices.
 </li>
 <li>
 Partner cross-functionally: Work with product and engineering teams to turn real asset-management problems into customer-facing ML solutions.
 </li>
 <li>
 Mentor &amp; lead: Provide technical guidance, review designs, and elevate ML engineering standards across the team.
 </li>
 <li>
 Analyze data deeply: Perform EDA on structured and unstructured data to uncover insights, patterns, and quality issues.
 </li>
 <li>
 Research domain signals: Investigate operational and asset datasets to identify trends, root causes, and predictive opportunities.
 
 
 </li>
 </ul>
 Qualifications:
 
 
 <ul>
 <li>
 B.Sc. in Computer Engineering or Electronics Engineering.
 </li>
 <li>
 10+ years in software/ML engineering, including 3+ years running production ML systems end-to-end.
 </li>
 <li>
 Hands-on experience building LLM applications (RAG, fine-tuning, prompt engineering, evaluators/guardrails, agent workflows) using tools like LangChain/LangGraph.
 </li>
 <li>
 Strong AWS background (3+ years), including core infrastructure and ML services such as SageMaker and Bedrock.
 </li>
 <li>
 Solid modeling stack: Python, PyTorch, Hugging Face, embeddings/vector databases, and LLM evaluation techniques.
 </li>
 <li>
 Proven MLOps experience: CI/CD for ML, experiment tracking, model registries, monitoring, and automated retraining pipelines.
 </li>
 <li>
 Good data engineering foundations: ETL/ELT pipelines, streaming/batch processing (Spark/Flink), and data governance practices.
 </li>
 <li>
 Experience with distributed training, RLHF, or optimization on specialized AI hardware is a plus!
 </li>
 <li>
 Preferrably has background in asset, sustainability, or intelligent operations domains.
 </li>
 <li>
 Familiarity with enterprise ML security and compliance practices is nice to have.
 
 
 </li>
 </ul>
 We&rsquo;re Siemens. A collection of over 377,000 minds building the future, one day at a time in over 200 countries. We're dedicated to equality, and we welcome applications that reflect the diversity of the communities we work in. All employment decisions at Siemens are based on qualifications, merit and business need. Bring your curiosity and creativity and help us shape tomorrow!
 
 
 Siemens is an equal opportunities employer and do not discriminate unlawfully on any grounds. We are committed to providing access and equal opportunity.
</div>

Principal Machine Learning Engineer

HireOn

<div>
 
 Senior MLOps Engineer (Onsite | Cairo, Egypt)
 
 
 
 
 Experience:
 
 5&ndash;8 Years
 
 
 
 Employment Type:
 
 Full-Time
 
 
 
 Location:
 
 Cairo, Egypt (Onsite)
 
 
 
 Salary Range:
 
 80,000 EGP &ndash; 100,000 EGP per month
 
 
 
 About The Opportunity
 
 
 
 HireOn is recruiting for its reputed international client seeking a highly skilled
 
 Senior MLOps Engineer
 
 to lead the operationalization of machine learning models in production environments.
 
 
 This role requires a hands-on expert who can bridge Data Science and Cloud Engineering teams, ensuring scalable, secure, and automated ML systems on AWS infrastructure. The ideal candidate will drive end-to-end MLOps architecture, CI/CD automation, and cloud-native ML deployment strategies.
 
 
 
 Core Requirements Experience
 
 
 
 <ul>
 <li>
 5&ndash;8 years of overall experience with minimum 3+ years in MLOps or production ML environments
 </li>
 <li>
 Strong experience managing the full ML lifecycle (training, deployment, monitoring, optimization)
 </li>
 <li>
 Proven ability to work independently and collaborate across cross-functional teams
 
 
 </li>
 </ul>
 
 AWS &amp; Cloud Expertise (Mandatory)
 
 
 
 
 Hands-on Experience With
 
 
 
 Amazon SageMaker, S3, EC2, Lambda, IAM, CloudWatch, ECR, ECS, EKS
 
 
 Strong understanding of secure, scalable, and highly available AWS architecture
 
 
 
 MLOps &amp; Machine Learning
 
 
 
 <ul>
 <li>
 Model deployment and monitoring in production
 </li>
 <li>
 Experience with TensorFlow, PyTorch, or Scikit-learn
 </li>
 <li>
 Experiment tracking tools such as MLflow
 </li>
 <li>
 Model performance monitoring and drift detection
 
 
 </li>
 </ul>
 
 DevOps &amp; Automation
 
 
 
 <ul>
 <li>
 Docker and containerization
 </li>
 <li>
 CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline
 </li>
 <li>
 Infrastructure as Code (Terraform or CloudFormation)
 
 
 </li>
 </ul>
 
 Programming &amp; Data
 
 
 
 <ul>
 <li>
 Strong Python programming expertise
 </li>
 <li>
 Experience with SQL and working knowledge of NoSQL databases
 </li>
 <li>
 Experience handling structured and unstructured datasets
 
 
 </li>
 </ul>
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and implement scalable end-to-end MLOps pipelines
 </li>
 <li>
 Deploy and manage ML models using AWS-native services
 </li>
 <li>
 Build and maintain CI/CD pipelines for ML workflows
 </li>
 <li>
 Implement model monitoring, logging, and performance tracking
 </li>
 <li>
 Containerize ML applications and deploy on ECS/EKS
 </li>
 <li>
 Automate infrastructure using Terraform or CloudFormation
 </li>
 <li>
 Ensure system scalability, reliability, and security
 </li>
 <li>
 Troubleshoot ML pipelines and cloud infrastructure issues
 </li>
 <li>
 Collaborate closely with Data Science and Engineering teams to productionize ML solutions
 
 
 </li>
 </ul>
 
 Nice to Have
 
 
 
 <ul>
 <li>
 Exposure to feature stores and data versioning
 </li>
 <li>
 AWS Associate-level certification
 </li>
 <li>
 Understanding of ML governance, compliance, and model risk management
 
 
 </li>
 </ul>
 Skills: aws codepipeline,terraform,tensorflow,aws sagemaker,mlops,python,aws,ml
</div>

Senior MLOps Engineer

Explore 416+ Senior machine learning engineer in Cairo Egypt this March. Find roles across top companies and apply now to boost your career.

416 senior machine learning engineer jobs In Cairo Egypt

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Data Scientist / Senior Machine Learning Engineer

Senior AI Engineer

Senior AI Engineer

Principal Machine Learning Engineer

Senior MLOps Engineer