accountant – accounts receivable (junior)

data engineer

ai engineer, light

ai platform engineer

devops engineer

senior backend engineer

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>

Finaira

<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

Beltone

<div>
 
 
 
 Job Overview:
 
 
 
 
 
 
 
 Designing, developing, and maintaining scalable backend systems, APIs, and applications that transform data science and analytics outputs into reliable production solutions and works closely with data scientists, data engineers, and analysts to produce machine learning models, analytics workflows, and data-driven insights into internal tools and client-facing products.
 
 
 
 
 
 
 
 Job Responsibilities:
 
 
 
 <ul>
 <li>
 Design and develop scalable backend services and APIs that support data-driven products and analytics platforms to enable seamless access to data, analytics, and machine learning capabilities through reliable and scalable system interfaces.
 </li>
 <li>
 Build robust systems that enable the integration of analytics workflows and machine learning models into applications to ensure that analytical insights and predictive models can be effectively utilized within operational business applications.
 </li>
 <li>
 Develop integrations with relational databases, APIs, and external systems to allow efficient data exchange and connectivity between internal platforms and external services.
 </li>
 <li>
 Ensure high system performance, reliability, and scalability of services to maintain stable and efficient system operations that support growing data and user demands. Collaborate with data scientists and data engineers to deploy and operationalize machine learning models and analytics pipelines to transition analytical models from experimentation into production-ready solutions that deliver business value.
 </li>
 <li>
 Build applications and services that expose data insights, analytics outputs, and machine learning predictions to users and other systems to make data-driven insights accessible and actionable for both internal stakeholders and external clients.
 </li>
 <li>
 Develop internal tools and client-facing solutions that interact with data platforms and services to enhance operational efficiency and provide user-friendly access to data capabilities.
 </li>
 <li>
 Containerize applications and manage deployments using modern DevOps practices to ensure consistent, scalable, and efficient application deployment across environments.
 </li>
 <li>
 Maintain CI/CD pipelines and ensure efficient software delivery process to enable continuous integration and deployment, improving development speed and software quality.
 </li>
 <li>
 Implement logging, monitoring, and alerting mechanisms to maintain system reliability to detect, diagnose, and resolve system issues quickly while maintaining service stability.
 </li>
 <li>
 Support production environments and troubleshoot system issues when required to ensure minimal system downtime and maintain smooth operational performance.
 </li>
 <li>
 Contribute to the design and architecture of scalable, production-grade systems to build reliable and maintainable software systems capable of supporting complex data products.
 </li>
 <li>
 Optimize system performance, API response times, and database queries to enhance system efficiency and provide fast, responsive services to users and applications.
 </li>
 <li>
 Ensure security and best practices in system development and deployment to protect data, systems, and infrastructure from vulnerabilities and security risks.
 </li>
 <li>
 Provide technical guidance and mentorship to junior engineers when needed to support team development, improve technical skills, and maintain engineering excellence.
 </li>
 <li>
 Contribute to improving engineering standards, development processes, and code quality to maintain high-quality software development practices and ensure long-term system sustainability.
 </li>
 </ul>
 
 
 
 
 
 
 Job Qualifications:
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in actuarial science, Mathematics, Statistics, Computer Science, Computer Engineering, or Business Informatics.
 </li>
 <li>
 Minimum 3+ years of experience in software engineering or backend development.
 </li>
 <li>
 Experience building and maintaining production-grade systems and APIs.
 </li>
 <li>
 Experience working with data-driven applications or analytics platforms is preferred.
 </li>
 <li>
 Strong programming experience in Python.
 </li>
 <li>
 Experience developing RESTful APIs and backend services.
 </li>
 <li>
 Strong knowledge of relational databases such as PostgreSQL or similar.
 </li>
 <li>
 Experience with Docker and containerized application deployment.
 </li>
 <li>
 Understanding of system scalability, performance optimization, and reliability.
 </li>
 <li>
 Experience working with version control systems (Git) and collaborative development workflows.
 </li>
 <li>
 Experience working with cloud platforms (AWS, GCP, or Azure).
 </li>
 <li>
 Experience with React for frontend development.
 </li>
 <li>
 Familiarity with TypeScript or modern JavaScript frameworks.
 </li>
 <li>
 Experience integrating frontend applications with backend APIs.
 </li>
 <li>
 Experience with Kubernetes or container orchestration platforms.
 </li>
 <li>
 Experience deploying machine learning models into production environments.
 </li>
 <li>
 Familiarity with data pipelines, ETL processes, or analytics platforms
 </li>
 </ul>
</div>

Senior Software Engineer

Pharco Corporation

<div>
 
 We are seeking a
 
 Data Scientist Specialist
 
 to join our team. The successful candidate will analyze large datasets, develop machine learning models, and provide insights that improve decision-making and business outcomes.
 
 
 
 
 &gt; Key Responsibilities
 
 
 
 <ul>
 <li>
 
 Data Collection &amp; Preparation:
 
 Gather, clean, and organize large volumes of structured and unstructured data.
 </li>
 <li>
 
 Statistical Analysis:
 
 Apply advanced statistical methods and data mining techniques to identify trends and correlations.
 </li>
 <li>
 
 Model Development:
 
 Build, test, and optimize predictive models and algorithms to forecast outcomes and improve performance.
 </li>
 <li>
 
 Experimentation:
 
 Design experiments to validate hypotheses and measure business impact.
 </li>
 <li>
 
 Collaboration:
 
 Work closely with cross-functional teams (engineering, product, marketing, operations) to implement models and monitor outcomes.
 </li>
 <li>
 
 Automation:
 
 Develop anomaly detection systems and automated tools for continuous monitoring.
 </li>
 <li>
 
 Visualization &amp; Reporting:
 
 Create dashboards, reports, and presentations to communicate insights clearly to stakeholders.
 </li>
 <li>
 
 Compliance:
 
 Document procedures and ensure adherence to data governance and security policies.
 </li>
 <li>
 
 Continuous Learning:
 
 Stay updated with emerging technologies, tools, and methodologies in data science.
 </li>
 </ul>
 
 
 
 &gt; Skills &amp; Qualifications
 
 
 
 <ul>
 <li>
 Strong foundation in
 
 statistics, mathematics, and probability
 
 .
 </li>
 <li>
 Proficiency in
 
 Python, R, SQL
 
 , and familiarity with big data tools (Hadoop, Spark).
 </li>
 <li>
 Experience with
 
 machine learning frameworks
 
 (TensorFlow, Scikit-learn, PyTorch).
 </li>
 <li>
 Knowledge of
 
 data visualization tools
 
 (Tableau, Power BI, matplotlib).
 </li>
 <li>
 Ability to translate technical findings into
 
 business-friendly insights
 
 .
 </li>
 <li>
 Excellent problem-solving, communication, and collaboration skills.
 </li>
 </ul>
 
 
</div>

Data Science Specialist

Valeo

<div>
 Valeo est une entreprise mondiale de haute technologie qui con&ccedil;oit des solutions r&eacute;volutionnaires pour r&eacute;inventer la mobilit&eacute;. Nous sommes un &eacute;quipementier partenaire des constructeurs automobiles et des acteurs de la nouvelle mobilit&eacute; dans le monde entier. Notre vision ? Inventer une mobilit&eacute; plus verte et plus s&ucirc;re, gr&acirc;ce &agrave; des solutions ax&eacute;es sur la conduite intuitive et la r&eacute;duction des &eacute;missions de CO2. Nous sommes leaders dans nos domaines d'activit&eacute; et reconnus comme l'une des plus grandes entreprises innovantes au monde.
 
 
 Responsibilities Cross-Disciplinary Automation
 
 ~
 
 Design and implement AI workflows that bridge the gap between Systems Engineering and downstream disciplines (HW, SW, Mech) to ensure seamless requirements traceability and consistency.
 
 
 Physics-Informed Modeling~ Develop and deploy Physics-Informed Machine Learning (PIML) models to accelerate simulations in Mechanics and Electrotechnique.
 
 
 
 AI-Enhanced System Engineering
 
 ~ Integrate AI-assisted coding, automated unit testing, and bug prediction tools into the software development pipeline in compliance with ASPICE standards.
 
 
 
 Predictive Project Management
 
 ~ Build predictive analytics dashboards for Technical Project Managers to forecast resource bottlenecks, budget risks, and milestone delays using historical project data.
 
 
 
 Standards &amp; Compliance~
 
 Ensure all AI-automated processes and generated outputs adhere to ISO 26262 (Functional Safety) and ISO/SAE 21434 (Cybersecurity) requirements.
 
 
 
 Toolchain Integration
 
 ~ Lead the integration of AI agents with existing toolchains, including PLM, ALM (Codebeamer), and MATLAB/Simulink.
 
 
 Key competencies
 
 Technical Skills
 
 
 
 AI and Machine Learning
 
 
 <ul>
 <li>
 Generative AI &amp; LLMs
 </li>
 <li>
 Predictive Modeling
 </li>
 <li>
 Computer Vision
 
 
 </li>
 </ul>
 Data Engineering and Management
 
 
 <ul>
 <li>
 Data Pipeline Construction~ Automating the flow of data into AI models.
 </li>
 <li>
 Data Quality Assurance~ Cleaning and labeling datasets to ensure the AI isn't learning from "noise."
 
 
 </li>
 </ul>
 
 Process Automation &amp; Integration (MLOps)
 
 
 
 <ul>
 <li>
 Workflow Orchestration~ Using tools to connect AI outputs to other R&amp;D software (like ELNs or LIMS).
 </li>
 <li>
 Deployment~ Ensuring the AI tools are accessible via user-friendly interfaces.
 
 
 </li>
 </ul>
 Programming &amp; Scripting~
 
 
 <ul>
 <li>
 Python
 </li>
 <li>
 Javascript / Google Apps Script
 </li>
 <li>
 Google Docs / Sheets functions &amp; automation
 </li>
 <li>
 HTTP rest API's
 
 
 </li>
 </ul>
 Tooling &amp; ALM~ ( Plus Knowledge)
 
 
 <ul>
 <li>
 Codebeamer (Requirement Management Tool)
 </li>
 <li>
 Google Cloud Platform
 
 
 </li>
 </ul>
 Automation &amp; Integration concepts
 
 
 <ul>
 <li>
 AI &amp; Data Handling~
 </li>
 <li>
 Basic to intermediate experience with AI tools, APIs, or AI agents
 </li>
 <li>
 Interest in applying AI to engineering processes
 
 
 </li>
 </ul>
 Soft Skills
 
 
 <ul>
 <li>
 Strong communication and collaboration skills, including ability to translate technical AI capabilities into tangible benefits for the teams
 </li>
 <li>
 Ability to work autonomously and proactively
 </li>
 <li>
 Analytical mindset with strong problem-solving skills
 </li>
 <li>
 User-oriented mindset (training, support, feedback handling)
 </li>
 <li>
 Time Managment
 
 
 </li>
 </ul>
 Nice to Have
 
 
 Experience with requirement traceability or compliance activities
 
 
 Exposure to AI-assisted automation or low-code/no-code platforms
 
 
 Physics-Informed Machine Learning (PIML)
 
 
 Predictive Project Analytics
 
 
 Understanding of system engineering lifecycle and deliverables
 
 
 
 Aware About Automotive Industry And Embedded Systems.
 
 
 
 Years of Experience 2 to 3 years experience in AI &amp; tools development
 
 
 
 Position~
 
 
 
 AI4all, Engineering Disciplines
 
 
 
 D&eacute;partement~
 
 
 
 GTSC Software
 
 
 
 Schedule~
 
 
 
 Temps plein
 
 
 
 Employee Status~
 
 
 
 CDI
 
 
 
 Type de contrat~
 
 
 
 
 Job Posting Date~
 
 
 
 2026-04-07
 
 
 
 Join Us !
 
 
 Being part of our team, you will join~
 
 
 <ul>
 <li>
 one of the largest global innovative companies, with more than 20,000 engineers working in Research &amp; Development
 </li>
 <li>
 a multi-cultural environment that values diversity and international collaboration
 </li>
 <li>
 more than 100,000 colleagues in 31 countries... which make a lot of opportunity for career growth
 </li>
 <li>
 a business highly committed to limiting the environmental impact if its activities and ranked by Corporate Knights as the number one company in the automotive sector in terms of sustainable development
 
 
 </li>
 </ul>
 More information on Valeo~ https~//www.valeo.com
 
</div>

AI & Automation Engineer, LIGHT

Faze 3 Consulting

<div>
 
 About Our Client
 
 
 Our client is an established management consulting firm working with government, financial services, and enterprise clients across the GCC and wider MENA region. Their identity will be disclosed at first contact from Faze 3.
 
 
 
 
 
 The Team You'd Be Joining
 
 
 A rapidly growing, ambitious AI engineering team being built from the ground up to design and ship the AI products at the centre of the client&rsquo;s consulting work. As an early hire in this build, you will help shape both the technical foundations and the engineering culture of the function.
 
 
 
 
 
 The Role
 
 
 This role bridges classical machine learning and modern LLM-based systems. You will build production ML models (regression, classification, time-series, anomaly detection) alongside hybrid architectures combining retrieval-augmented generation, ML scoring, and rules-based logic. You will own the evaluation frameworks that keep applied AI honest in production: precision, recall, hypothesis testing, model diagnostics, drift detection.
 
 
 This is a hands-on practitioner role, not research. The team ships into live client engagements against measured precision, recall, latency, and cost targets.
 
 
 
 
 
 Mandatory Requirements
 
 
 
 Education.
 
 Bachelor&rsquo;s in Computer Science (or very similar/related) OR Bachelor&rsquo;s in Statistics (or very similar/related) from a Tier 1 / Tier 2 university. Master&rsquo;s preferred. &ldquo;Very similar/related&rdquo; includes Software Engineering, Mathematics, Data Science, Physics, Applied Statistics, Information Systems, Econometrics, and similar substantively quantitative disciplines.
 
 
 
 
 
 
 Experience.
 
 5&ndash;10 years in applied ML / AI engineering, with at least 3 years building ML models that ran in production.
 
 
 
 
 
 
 Essential Skills:
 
 
 <ul>
 <li>
 Strong Python
 </li>
 <li>
 NumPy
 </li>
 <li>
 pandas
 </li>
 <li>
 scikit-learn
 </li>
 <li>
 production ML pipelines
 </li>
 <li>
 SQL
 </li>
 <li>
 working comfort with the LLM and RAG interplay.
 </li>
 </ul>
 
 
 
 
 
 Statistical rigour.
 
 Working fluency with hypothesis testing (t-tests, z-tests, chi-square), time-series and stationarity diagnostics (AD, JB, ARCH, ADF, KPSS), and model diagnostics (AIC, BIC, log-likelihood).
 
 
 
 
 
 
 Conditional certification.
 
 If your degree is in Statistics (or very similar/related), the role requires either the Stanford Machine Learning Specialization OR the IBM Python for Data Science and AI certification. For CS-pathway candidates, these are good-to-have.
 
 
 
 
 
 
 Languages.
 
 Proficiency in English is required.
 
 
 
 
 
 Desirable Skills
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Time-series modelling depth &mdash; ARIMA family, Prophet, deep-learning sequence models.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;PyTorch or TensorFlow at working level.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;MLflow or equivalent experiment tracking in production use.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Embeddings, vector search, and reranking experience.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;XGBoost, LightGBM, or CART model fluency.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Drift detection, shadow evaluation, or A/B test design experience.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A live GitHub or portfolio with original ML work.
 
 
 &bull;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Arabic language capability.
 
 
 
 
 
 How to Apply
 
 
 You will need your CV and a 300-word narrative describing one ML model you took from concept to production &mdash; problem, approach, evaluation, deployment, and measurable impact. A GitHub or portfolio link is welcomed but optional.
 
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Applied ML and AI Engineer

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