senior ai engineer

senior software engineer

data engineer

full stack engineer

senior data engineer

machine learning engineer

Si-Ware Systems

<div>
 Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
 
 
 At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
 
 
 As a
 
 Machine Learning Engineer
 
 at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
 
 
 You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
 
 
 initiatives.
 
 
 
 Responsibilities:
 
 
 
 
 Machine Learning &amp; Modeling
 
 
 
 <ul>
 <li>
 Perform data cleaning, preprocessing, and transformation for training and evaluation.
 </li>
 <li>
 Contribute to the development, training, and improvement of machine learning models.
 </li>
 <li>
 Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).
 </li>
 <li>
 Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).
 </li>
 <li>
 Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.
 </li>
 <li>
 Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.
 </li>
 <li>
 Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.
 
 
 
 </li>
 </ul>
 
 Production &amp; System Integration
 
 
 
 <ul>
 <li>
 Design and implement production-ready ML pipelines integrated with software applications and hardware systems.
 </li>
 <li>
 Ensure models are maintainable, version-controlled, and deployable within real-world products.
 </li>
 <li>
 Contribute to the design and evolution of ML system architecture and reusable pipeline components.
 </li>
 <li>
 Develop unit and integration tests for ML components to ensure reliability.
 </li>
 <li>
 Document ML modules and interfaces to support long-term maintainability.
 </li>
 <li>
 Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.
 
 
 
 </li>
 </ul>
 
 Tools, Innovation &amp; Growth
 
 
 
 <ul>
 <li>
 Participate in the development of internal and customer-facing ML-driven tools and automation utilities.
 </li>
 <li>
 Stay updated with the latest machine learning research, tools, and industry practices.
 </li>
 <li>
 Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.
 </li>
 <li>
 Bachelor&rsquo;s degree in Computer Science, Engineering, Mathematics, or a related field.
 </li>
 <li>
 1 - 4 years of relevant industry or applied ML experience preferred.
 </li>
 <li>
 Strong focus on applied machine learning and engineering implementation.
 </li>
 <li>
 Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).
 </li>
 <li>
 Familiarity with at least one deep learning framework (PyTorch or TensorFlow).
 </li>
 <li>
 Solid understanding of software engineering principles (modular design, version control, testing, debugging).
 </li>
 <li>
 Experience structuring modular Python codebases.
 </li>
 <li>
 Familiarity with packaging, model serialization, and reproducible pipelines.
 </li>
 <li>
 Experience working within larger software systems (not only notebooks).
 </li>
 <li>
 Strong analytical and problem-solving skills.
 </li>
 <li>
 Good communication skills and ability to work in a collaborative team environment.
 </li>
 <li>
 Proficiency in English (reading and writing).
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).
 </li>
 <li>
 Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).
 </li>
 <li>
 Experience contributing to ML tools, internal platforms, or data analysis software.
 </li>
 <li>
 Knowledge of MLOps practices (Git, CI/CD, Docker).
 </li>
 <li>
 Experience with cloud platforms (AWS, GCP, Azure).
 </li>
 <li>
 Experience with robotics frameworks (ROS).
 </li>
 <li>
 Experience with sensor fusion.
 </li>
 <li>
 Experience with real-time ML inference.
 </li>
 <li>
 Basic understanding of control systems.
 </li>
 <li>
 Exposure to data visualization or BI tools.
 </li>
 <li>
 Contributions to Kaggle competitions, research projects, or open-source initiatives.
 </li>
 </ul>
</div>

Machine Learning Engineer

Si-ware Systems

<div>Si-Ware Systems is a global leader in semiconductor and spectroscopy solutions. Our innovative devices and software enable material analysis across many industries.
At Si-Ware, we foster a culture of innovation, collaboration, and continuous learning, empowering our people to push the boundaries of technology.
As a Machine Learning Engineer at Si-Ware, you will design, implement, and deploy applied ML solutions that power real-world spectroscopy devices and emerging physical AI systems.
You will work at the intersection of machine learning, software engineering, and intelligent hardware integration.
initiatives.<h4 class="jobSectionHeader">
Responsibilities:</h4>
Machine Learning &amp; Modeling<ul><li>
Perform data cleaning, preprocessing, and transformation for training and evaluation.</li></ul><ul><li>
Contribute to the development, training, and improvement of machine learning models.</li><li>
Design and execute structured experiments using appropriate validation strategies (cross-validation, hold-out testing, statistical comparison).</li><li>
Evaluate models using appropriate performance metrics (Accuracy, Precision, Recall, F1, RMSE, etc.).</li><li>
Optimize models and inference pipelines for performance, memory efficiency, and real-time constraints when required.</li><li>
Ensure reproducibility, traceability, and proper validation of models within regulated or industrial environments.</li><li>
Support chemometrics-related workflows, including spectral preprocessing, feature extraction, multivariate modeling, and validation for spectroscopy-based applications.</li></ul>
Production &amp; System Integration<ul><li>
Design and implement production-ready ML pipelines integrated with software applications and hardware systems.</li><li>
Ensure models are maintainable, version-controlled, and deployable within real-world products.</li><li>
Contribute to the design and evolution of ML system architecture and reusable pipeline components.</li><li>
Develop unit and integration tests for ML components to ensure reliability.</li><li>
Document ML modules and interfaces to support long-term maintainability.</li><li>
Work closely with software and firmware teams to integrate ML models into desktop applications, services, and embedded workflows.</li></ul>
Tools, Innovation &amp; Growth<ul><li>
Participate in the development of internal and customer-facing ML-driven tools and automation utilities.</li><li>
Stay updated with the latest machine learning research, tools, and industry practices.</li><li>
Contribute to exploratory and applied ML solutions in emerging physical AI systems, including robotics and sensor-driven intelligent platforms.</li></ul><ul><li>
Bachelor&rsquo;s degree in Computer Science, Engineering, Mathematics, or a related field.</li><li>
1 - 4 years of relevant industry or applied ML experience preferred.</li><li>
Strong focus on applied machine learning and engineering implementation.</li><li>
Hands-on experience with Python and its ML ecosystem (NumPy, Pandas, Matplotlib, Scikit-Learn).</li><li>
Familiarity with at least one deep learning framework (PyTorch or TensorFlow).</li><li>
Solid understanding of software engineering principles (modular design, version control, testing, debugging).</li><li>
Experience structuring modular Python codebases.</li><li>
Familiarity with packaging, model serialization, and reproducible pipelines.</li><li>
Experience working within larger software systems (not only notebooks).</li><li>
Strong analytical and problem-solving skills.</li><li>
Good communication skills and ability to work in a collaborative team environment.</li><li>
Proficiency in English (reading and writing).</li></ul><h4 class="jobSectionHeader">
Nice to Have:</h4><ul><li>
Basic understanding of chemometrics concepts (multivariate analysis, regression/classification, spectral data handling).</li><li>
Exposure to spectroscopy data (NIR, IR) and common preprocessing techniques (normalization, smoothing, baseline correction).</li><li>
Experience contributing to ML tools, internal platforms, or data analysis software.</li><li>
Knowledge of MLOps practices (Git, CI/CD, Docker).</li><li>
Experience with cloud platforms (AWS, GCP, Azure).</li><li>
Experience with robotics frameworks (ROS).</li><li>
Experience with sensor fusion.</li><li>
Experience with real-time ML inference.</li><li>
Basic understanding of control systems.</li><li>
Exposure to data visualization or BI tools.</li><li>
Contributions to Kaggle competitions, research projects, or open-source initiatives.</li></ul></div>

YO IT CONSULTING

<div><div><div>Engagement Type: Independent Contractor 
Work Mode: Fully Remote 
Contract Type: Project-Based (Short-Term, Extendable)
Role Overview:
collaborating with a leading AI research lab to support the evaluation of advanced machine learning systems. We are seeking experienced machine learning engineers and researchers to contribute to the design of high-quality evaluation suites that measure AI performance on real-world machine learning engineering tasks. The work focuses on translating practical ML research and engineering workflows into structured benchmarks for frontier models. This is a project-based, remote opportunity suited for experts with hands-on ML research experience.
Key responsibilities
<ul><li>Design and write detailed evaluation suites for machine learning engineering tasks
</li><li>Assess AI-generated solutions across areas such as model training, debugging, optimization, and experimentation
</li></ul>Ideal qualifications
<ul><li>3+ years of experience in machine learning engineering or applied ML research
</li><li>Hands-on experience with model development, experimentation, and evaluation
</li><li>Background in ML research (industry lab or academic setting strongly preferred)
</li><li>Strong ability to reason about ML system design choices and tradeoffs
</li><li>Clear written communication and high attention to technical detail
</li></ul>We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
</div></div><div><div><h2 class="jobSectionHeader">Contract and Payment Terms</h2>
<ul><li>You will be engaged as an independent contractor.</li>
<li>This is a fully remote role that can be completed on your own schedule.</li>
<li>Projects can be extended, shortened, or concluded early depending on needs and performance.</li>
<li>Your work at will not involve access to confidential or proprietary information from any employer, client, or institution.</li>
<li>Payments are weekly on Stripe or Wise based on services rendered.</li></ul></div></div></div>

Smart Eye

<div>
 Smart Eye is the leading provider of Human Insight AI, technology that understands, supports and predicts human behavior in complex environments. The company is on a mission to bridge the gap between humans and machines for a safe and sustainable future. Supported by Affectiva and iMotions &ndash; companies it acquired in 2021 &ndash; Smart Eye&rsquo;s multimodal software and hardware solutions provide unparalleled insight into human behavior.
 
 
 In automotive, Smart Eye&rsquo;s driver monitoring systems and interior sensing solutions improve road safety and the mobility experience. The company&rsquo;s eye tracking technology and iMotions biosensor software platform are also used in behavioral research to enable advanced research in academic and commercial sectors. In media analytics, Affectiva&rsquo;s Emotion AI provides the world&rsquo;s largest brands and market researchers with a deeper understanding of how consumers engage with content, products, and services.
 
 
 Founded in 1999, Smart Eye is a global company headquartered in Sweden, with customers including NASA, Nissan, Boeing, Honeywell, Volvo, GM, BMW, Polestar, Geely, Harvard University, 26 percent of the Fortune Global 500 companies, and over 1,300 research organizations around the world.
 
 
 Visit
 
 www.smarteye.ai
 
 for more information.
 
 
 You will be part of one of Smart Eye's algorithm development teams responsible for development in key areas such as head- and eye-tracking, attentiveness and drowsiness, face identification and interior sensing for our Automotive Cabin and Driving Monitoring product. We work in an international environment with colleagues in Gothenburg (SE) and Boston (US).
 
 
 We are building safety features in novel technology that will significantly improve road safety and will transform our transportation experiences. Safety features such as determining people&rsquo;s seating locations in a vehicle, if they are wearing seat belts, and detection of left-behind children, as well as features that enhance comfort, wellness, and entertainment by detecting occupants&rsquo; emotions and gestures.
 
 
 Development activities range from early-stage prototyping to porting and deployment to a wide set of automotive embedded platforms. And you will be responsible for the complete chain of development activities including protocols for data collection, network architecture, training of networks, exploratory data analysis and visualization, but also integration into and validation of the model in the production software.
 
 
 We solve these challenging problems by utilizing state-of-the-art algorithms in the fields of image processing, the latest machine learning techniques, mathematics, and accelerated computing.
 
 
 You will have an active impact on the processes and projects and will work in close cooperation with other talented team members. Your contribution to how we can meet our customers' needs with tomorrow's technology will be of crucial importance.
 
 
 Who are you?
 
 
 <ul>
 <li>
 We welcome all types of personalities and believe in a true mix of people to create a dynamic work environment.
 </li>
 <li>
 You're able to work independently in an environment that changes rapidly.
 </li>
 <li>
 Exhibit a positive, enthusiastic, open, entrepreneurial attitude and a willingness to work closely with customers or other teams within Smart Eye.
 </li>
 <li>
 Possess strong analytical, creative skills with a strong interest in new technologies.
 
 
 </li>
 </ul>
 
 Must-have-skills
 
 
 
 <ul>
 <li>
 Degree in Electrical Engineering, Computer Sciences, or Mathematics with specialization in computer vision or machine learning.
 </li>
 <li>
 Experience using end-to-end development of machine learning based systems (design, planning, evaluation, deployment).
 </li>
 <li>
 3+ years of software development experience.
 </li>
 <li>
 3+ years of work experience in C++ and Python development.
 </li>
 <li>
 Strong communication and team collaboration skills.
 
 
 </li>
 </ul>
 
 Good-to-have-skills
 
 
 
 <ul>
 <li>
 Experience in optimizing Deep Learning model architectures and inference runtimes (ex. Tensorflow, Tensorflow Lite, PyTorch).
 </li>
 <li>
 Experience in one or more fields of image processing or graphics programming.
 </li>
 <li>
 Experience with CI/CD (Continuous Integration and Deployment).
 </li>
 <li>
 Experience in developing software for the Automotive industry.
 </li>
 <li>
 Familiar with Agile methodologies (has worked with Git, Jira and alike).
 </li>
 </ul>
</div>

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

<div>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> 
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:Transformer architectures (e.g., BERT, GPT, T5, LLama, Mistral)Large Language Models (LLMs) and their fine-tuning/adaptationVector embeddings and similarity searchText classification, named entity recognition (NER), sentiment analysis, summarization, and question answering.</li><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>

Siemens

<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.
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 Drive MLOps &amp; governance: Build CI/CD pipelines for ML, manage model/version registries, track experiments, and enforce responsible AI practices.
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 Partner cross-functionally: Work with product and engineering teams to turn real asset-management problems into customer-facing ML solutions.
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 Mentor &amp; lead: Provide technical guidance, review designs, and elevate ML engineering standards across the team.
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 Analyze data deeply: Perform EDA on structured and unstructured data to uncover insights, patterns, and quality issues.
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 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.
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 Hands-on experience building LLM applications (RAG, fine-tuning, prompt engineering, evaluators/guardrails, agent workflows) using tools like LangChain/LangGraph.
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 Strong AWS background (3+ years), including core infrastructure and ML services such as SageMaker and Bedrock.
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 Solid modeling stack: Python, PyTorch, Hugging Face, embeddings/vector databases, and LLM evaluation techniques.
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 Proven MLOps experience: CI/CD for ML, experiment tracking, model registries, monitoring, and automated retraining pipelines.
 </li>
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 Good data engineering foundations: ETL/ELT pipelines, streaming/batch processing (Spark/Flink), and data governance practices.
 </li>
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 Experience with distributed training, RLHF, or optimization on specialized AI hardware is a plus!
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 Preferrably has background in asset, sustainability, or intelligent operations domains.
 </li>
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 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

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