qa/qc engineer

mechanical engineer

sales engineer

electrical engineer

planning engineer

machine learning engineer

Codvo.ai

<div><div><div>Data Scientist Consultant (Category &ndash; Senior Engineer)
Sector: Oil and Gas
Department: Digital Transformation
Reporting To: Head of Digital Intelligence
About Us
At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology. 
Job Description
The Data Science Consultant acts as the strategic link between business problem solving and advanced analytics. By applying machine learning and statistical rigor to complex business challenges, this role identifies key opportunities to optimize safety, efficiency, and overall performance.
Key Responsibilities<ul><li>Identify and support analytics and AI use cases aligned with business and conceptualization priorities.</li><li>Engage stakeholders to gather requirements and translate business needs into analytical problem statements.</li><li>Perform exploratory data analysis, statistical modelling, and predictive analytics on structured and unstructured datasets.</li><li>Develop machine learning models and analytical solutions to support forecasting, optimization, and anomaly detection.</li><li>Design dashboards and performance reporting frameworks to support decision-making.</li><li>Conduct impact analysis, ROI estimation, and value tracking for analytics initiatives.</li><li>Collaborate with data engineers and product teams to deploy scalable analytics solutions.</li><li>Present insights and recommendations to both technical teams and business leadership.</li></ul>
Required Skills and Qualifications<ul><li>+6 years of experience in data science, analytics, or business intelligence roles under digital transformation projects.</li><li>Strong proficiency in Python, SQL, and Power BI or similar visualization tools.</li><li>Experience with machine learning frameworks (Scikit-learn, TensorFlow, or PyTorch).</li><li>Strong analytical thinking and structured problem-solving skills.</li><li>Experience in requirements gathering, stakeholder engagement, and business case development.</li><li>Ability to communicate complex analytical findings in clear business terms.</li></ul>
Preferred Qualifications<ul><li>Experience in Oil &amp; Gas, energy, or industrial analytics environments.</li><li>Familiarity with enterprise data platforms such as Azure or Databricks.</li><li>Knowledge of Agile delivery frameworks.</li><li>Certification in Data Analytics, Business Analysis, or Data Science preferred.</li></ul> 
Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered</div></div></div>

Data Scientist Consultant (Category – Senior Engineer) - Doha Qatar

Commercial Bank

<div>
 
 About Commercial Bank Of Qatar
 
 
 
 Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar&rsquo;s economic development by offering a range of personal, business, government, international and investment services.
 
 
 We believe in empowering our employees, providing them with opportunities for growth and professional development.
 
 
 By Joining us, you&rsquo;ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.
 
 
 Join us in shaping the future of banking!
 
 
 
 Job Summary
 
 
 
 <ul>
 <li>
 Responsible for developing advanced machine learning models for banking use cases including pricing optimization, customer propensity modelling, and recommendation systems.
 </li>
 <li>
 This senior role requires deep expertise in statistical modelling and machine learning combined with substantial banking and financial services domain knowledge .
 </li>
 <li>
 The position focuses on translating complex business problems in areas such as Risk, Finance, Retail Banking, and Wholesale Banking into actionable ML solutions.
 </li>
 <li>
 Will leverage their understanding of banking products, regulatory requirements, and financial metrics to build models that drive measurable business value.
 </li>
 <li>
 The role requires a balance of domain expertise and technical capability, with sufficient programming skills in Python and SQL to develop and deliver working prototypes that can be transitioned to production.
 
 
 </li>
 </ul>
 
 
 Key Accountabilities
 
 
 
 
 <ul>
 <li>
 ML Model Development
 </li>
 <li>
 Design and develop machine learning models for pricing optimization, including dynamic pricing, rate optimization, and fee structures.
 </li>
 <li>
 Build propensity models for customer behavior prediction including churn, cross-sell, upsell, and product adoption.
 </li>
 <li>
 Develop recommendation systems for personalized product offerings, next-best-action, and customer engagement.
 </li>
 <li>
 Banking Domain Application
 </li>
 <li>
 Apply deep banking domain knowledge to frame business problems as ML solutions with measurable outcomes.
 </li>
 <li>
 Partner with Risk, Finance, and business units to identify high-value modeling opportunities.
 </li>
 <li>
 Ensure models incorporate relevant regulatory requirements, risk considerations, and business constraints.
 </li>
 <li>
 Analysis &amp; Insights
 </li>
 <li>
 Conduct exploratory data analysis to identify patterns, relationships, and modeling opportunities in banking data.
 </li>
 <li>
 Translate model outputs into actionable business recommendations and insights.
 </li>
 <li>
 Develop model performance metrics aligned with business KPIs and financial outcomes.
 </li>
 <li>
 Create data visualizations and reports for stakeholder communication.
 </li>
 <li>
 Prototyping &amp; Delivery
 </li>
 <li>
 Develop working prototypes in Python demonstrating model functionality and business value.
 </li>
 <li>
 Create clear documentation of model methodology, assumptions, limitations, and use cases.
 </li>
 <li>
 Collaborate with ML Engineers and AI Engineers to transition prototypes to production systems.
 </li>
 <li>
 Stakeholder Collaboration
 </li>
 <li>
 Partner with business stakeholders to understand requirements and validate model outputs.
 </li>
 <li>
 Present model results, methodology, and recommendations to senior management.
 </li>
 <li>
 Contribute to model governance, validation, and documentation requirements.
 </li>
 <li>
 Ensure compliance with data policies, ethical standards, and regulatory requirements.
 
 
 </li>
 </ul>
 
 Key Competencies
 
 
 
 <ul>
 <li>
 Machine Learning &amp; Statistics
 </li>
 <li>
 Expert knowledge of supervised and unsupervised learning techniques for classification, regression, and clustering.
 </li>
 <li>
 Deep experience with pricing models, propensity modeling, and recommendation systems.
 </li>
 <li>
 Strong foundation in statistical analysis, hypothesis testing, and experimental design.
 </li>
 <li>
 Familiarity with deep learning frameworks (TensorFlow, PyTorch) for advanced use cases.
 </li>
 <li>
 Banking Domain Expertise
 </li>
 <li>
 Comprehensive understanding of banking products (Retail or Corporate business), services, and customer lifecycle.
 </li>
 <li>
 Knowledge of Risk functions including credit risk, market risk, and operational risk frameworks.
 </li>
 <li>
 Understanding of Finance functions including P&amp;L drivers, cost allocation, and profitability analysis.
 </li>
 <li>
 Familiarity with regulatory requirements affecting model development (IFRS 9, Basel, etc.).
 </li>
 <li>
 Technical Skills
 </li>
 <li>
 Python for data analysis and model development (pandas, scikit-learn, XGBoost, etc.).
 </li>
 <li>
 SQL &ndash; Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries).
 </li>
 <li>
 Experience with data visualization and reporting tools.
 </li>
 <li>
 Familiarity with Git (GitHub/GitLab) for version control.
 </li>
 <li>
 Basic understanding of Spark for large-scale data processing.
 </li>
 <li>
 Awareness of MLOps practices and model deployment concepts (MLflow, TFX).
 </li>
 <li>
 Communication &amp; Collaboration
 </li>
 <li>
 Ability to translate complex analytical concepts into business language for non-technical stakeholders.
 </li>
 <li>
 Strong presentation skills for executive-level communication.
 </li>
 <li>
 Experience working with cross-functional teams across business and technology.
 </li>
 <li>
 Agile methodologies (Kanban, Scrum) experience.
 
 
 </li>
 </ul>
 
 Qualifications &amp; Experience
 
 
 
 <ul>
 <li>
 Master's degree or PhD in Finance, Economics, Statistics, Mathematics, or quantitative field strongly preferred.
 </li>
 <li>
 8+ years of experience in data science or quantitative analysis roles.
 </li>
 <li>
 Minimum 5 years of experience in banking or financial services industry is mandatory.
 </li>
 <li>
 Proven track record of delivering ML models in pricing, propensity, or recommendation domains.
 </li>
 <li>
 Background in Risk, Finance, or quantitative functions within banking preferred.
 </li>
 <li>
 Experience with model validation, governance, and regulatory requirements in financial services.
 </li>
 <li>
 Professional certifications in Risk (FRM, PRM) or Finance (CFA) are a plus.
 
 
 </li>
 </ul>
 
 
 Why Commercial Bank?
 
 
 
 
 <ul>
 <li>
 Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance.
 </li>
 <li>
 An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking
 </li>
 <li>
 Opportunities for Global Partnerships &amp; International Exposure, connecting employees with global networks and perspectives.
 </li>
 <li>
 A focus on Employee Well-being &amp; Work-Life Balance, ensuring a healthy and supportive environment for all team members
 </li>
 <li>
 Competitive Compensation &amp; Benefits that ensure our employees are rewarded for their dedication and performance
 </li>
 <li>
 A strong Commitment to Diversity, Equity &amp; Inclusion, fostering a culture that values every individual&rsquo;s unique perspective.
 
 
 </li>
 </ul>
 At Commercial Bank, we don&rsquo;t just offer careers, We shape futures by pioneering
 
 digital transformation
 
 in Qatar&rsquo;s banking sector, blending
 
 digital-first
 
 approach to redefine banking through
 
 innovative
 
 solutions.
 
 
 
 Disclaimer
 
 
 
 We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.
</div>

Senior Data Science Expert

<div>
 
 About Commercial Bank Of Qatar
 
 
 
 Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar&rsquo;s economic development by offering a range of personal, business, government, international and investment services.
 
 
 We believe in empowering our employees, providing them with opportunities for growth and professional development.
 
 
 By Joining us, you&rsquo;ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.
 
 
 Join us in shaping the future of banking!
 
 
 
 Job Summary
 
 
 
 Commercial Bank's Intelligence Unit (CBIU) is a diverse cross functional team providing data, analytics, and business intelligence to the bank.
 
 
 The team works on a diverse universe of data use cases with the objective to enable the Bank to take data driven business decisions, enhance efficiency and optimize returns.
 
 
 At Commercial Bank, data is a key driver in strategic decision making; the incumbent is required to have a thorough understanding of data analytics, data management principles and have ability to help the bank derive maximum value from the data available.
 
 
 Previous experience with data science and advanced analytical techniques, as well as AI and Machine Learning in a banking context would be a major success factor.
 
 
 The skills needed for the Data Scientist include technical knowledge and problem-solving and focus on delivery.
 
 
 The prospective candidate is expected to help take the bank&rsquo;s data science capabilities to the next level; building on a good foundation but accelerating both the capability development and the exploitation of benefits.
 
 
 
 
 Key Accountabilities
 
 
 
 
 The Data Scientist works with business executives and various departments within the Bank to provide advanced data science solutions for implementation.
 
 
 Use cases can include but would not be limited to intelligent character recognition, computer vision, behavioural analyses, early warning signs analyses, generative artificial intelligence, NLP, LLM, hyper-personalisation etc.
 
 
 The Data Scientist designs, tests and launches innovative and complex analytic models, utilizing a blend of contemporary and traditional data mining techniques, which he applies to both structured and unstructured data sets.
 
 
 The Data Scientist works on the development of big data capabilities and utilization as well as the coordination of cross-functional analytic initiatives.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Work on data mining and collection procedures
 </li>
 <li>
 Ensure data quality and integrity
 </li>
 <li>
 Interpret and analyze data problems
 </li>
 <li>
 Conceive, plan and prioritize data projects
 </li>
 <li>
 Build analytic systems, predictive models and artificial intelligence capabilities
 </li>
 <li>
 Test performance of data-driven products
 </li>
 <li>
 Visualize data and create innovative reports and present to key stakeholders including most senior Management of the Bank
 </li>
 <li>
 Experiment with new models and techniques
 
 
 </li>
 </ul>
 
 Key Competencies
 
 
 
 <ul>
 <li>
 Qualification as a Data Scientist
 </li>
 <li>
 Solid understanding of machine learning
 </li>
 <li>
 Knowledge of data management, advanced analytics and visualization techniques
 </li>
 <li>
 A knack for statistical analysis and predictive modeling
 </li>
 <li>
 Experiences with Artificial Intelligence
 </li>
 <li>
 Good knowledge of R, Python and MATLAB
 </li>
 <li>
 Experience with SQL and NoSQL databases
 </li>
 <li>
 Excellent communications skills; both oral and written/presentations
 </li>
 <li>
 A business mindset - Advanced Mathematics skills
 </li>
 <li>
 Demonstrate exceptional inclusive people interpersonal skills, ongoing professional development and commitment to Commercial Bank values
 </li>
 <li>
 Strong strategic thinking skills and the ability to frame problems
 </li>
 <li>
 Solid understanding of IT system architecture
 </li>
 <li>
 Experience with modern data quality management tools in the context of data quality / governance is a plus
 </li>
 <li>
 Familiarity with automated data quality management, data sharing, data traceability for business intelligence, and data warehouse re-engineering is a plus.
 </li>
 <li>
 Experience in the Financial Services industry and an understanding of key banking principles is a plus.
 
 
 </li>
 </ul>
 
 
 Why Commercial Bank?
 
 
 
 
 <ul>
 <li>
 Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance.
 </li>
 <li>
 An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking
 </li>
 <li>
 Opportunities for Global Partnerships &amp; International Exposure, connecting employees with global networks and perspectives.
 </li>
 <li>
 A focus on Employee Well-being &amp; Work-Life Balance, ensuring a healthy and supportive environment for all team members
 </li>
 <li>
 Competitive Compensation &amp; Benefits that ensure our employees are rewarded for their dedication and performance
 </li>
 <li>
 A strong Commitment to Diversity, Equity &amp; Inclusion, fostering a culture that values every individual&rsquo;s unique perspective.
 
 
 </li>
 </ul>
 At Commercial Bank, we don&rsquo;t just offer careers, We shape futures by pioneering
 
 digital transformation
 
 in Qatar&rsquo;s banking sector, blending
 
 digital-first
 
 approach to redefine banking through
 
 innovative
 
 solutions.
 
 
 
 Disclaimer
 
 
 
 We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.
</div>

Data Scientist

Al Adyat consultancy

Job Profile: AI Expert for Plant Data Model (PDM Project)Job SummaryWe are seeking a highly skilled AI Expert to design, build, and operationalize AI/ML solutions that deliver measurable business outcomes. The ideal candidate combines deep machine learning expertise with strong software engineering practices, MLOps proficiency, and stakeholder collaboration. This role will lead end-to-end projects&mdash;from problem framing and data acquisition to model development, deployment, monitoring, and continuous improvement&mdash;while ensuring compliance with security, privacy, and governance policies.Key Responsibilities&middot; Partner with business stakeholders to translate strategic objectives into AI use cases with clear KPIs and ROI.&middot; Own the full ML lifecycle: data sourcing, feature engineering, model selection, training, evaluation, and production deployment.&middot; Implement robust MLOps: CI/CD for ML, model registry, automated testing, reproducible pipelines, and monitoring (drift, performance, fairness).&middot; Design scalable data/feature pipelines using best practices for data quality, lineage, and governance.&middot; Develop models across domains (e.g., predictive analytics, time series, NLP, computer vision, recommender systems).&middot; Optimize models for efficiency and cost across CPU/GPU, leveraging quantization, distillation, or serving optimizations.&middot; Deploy and maintain models on cloud and on-prem platforms; orchestrate with containers and microservices (Docker/Kubernetes).&middot; Establish and enforce responsible AI practices: privacy-preserving methods, bias detection/mitigation, explainability, and secure handling of data.&middot; Produce high-quality documentation: design docs, runbooks, experiment logs, and user guides; present results to technical and non-technical audiences.&middot; Mentor engineers and analysts; contribute to standards, templates, and reusable components within the AI platform.Minimum Qualifications&middot; Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, or related field (or equivalent experience).&middot; 5+ years building and deploying ML models in production environments.&middot; Strong programming skills in Python and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).&middot; Hands-on experience with data processing frameworks (e.g., Pandas, Spark), SQL/NoSQL databases, and feature stores.&middot; Proficiency in MLOps tooling (e.g., MLflow, DVC, Kubeflow, Airflow) and CI/CD for ML.&middot; Experience deploying to cloud platforms (e.g., Azure Machine Learning, AWS SageMaker, GCP Vertex AI) and container orchestration (Docker/Kubernetes).&middot; Solid understanding of statistics, experiment design (A/B testing), and model validation techniques.&middot; Excellent communication and stakeholder management skills; ability to articulate complex concepts clearly.Preferred Qualifications&middot; Master&rsquo;s or PhD in a quantitative field.&middot; Experience with NLP (transformers, LLMs, RAG) and computer vision (CNNs, detection/segmentation).&middot; Exposure to time-series forecasting, optimization, and reinforcement learning for industrial use cases.&middot; Experience with Azure ecosystem (Azure ML, Databricks, Data Factory, Synapse) and enterprise integration patterns.&middot; Familiarity with privacy-preserving techniques (differential privacy, federated learning) and model interpretability (SHAP, LIME).&middot; Track record of delivering AI solutions in manufacturing, energy, or operations (e.g., predictive maintenance, process optimization).Technical Stack &amp; Tools&middot; Languages: Python (preferred), SQL; familiarity with C++/Java is a plus.&middot; ML/AI: scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face; experiment tracking (MLflow).&middot; Data: Pandas, Spark, Delta/Parquet; data versioning (DVC); feature stores.&middot; Pipelines &amp; Orchestration: Airflow, Prefect, Kubeflow; Docker/Kubernetes for serving.&middot; Monitoring &amp; Observability: Prometheus/Grafana; model drift/performance monitoring tools.&middot; DevOps: Git, GitHub/GitLab, CI/CD, testing frameworks (pytest).&middot; Cloud: Azure (preferred), AWS, GCP; serverless and managed ML services.Core Competencies &amp; Soft Skills&middot; Business acumen with the ability to prioritize use cases based on value and feasibility.&middot; Structured problem solving and hypothesis-driven experimentation.&middot; Clear written and verbal communication, storytelling with data, and visualization skills.&middot; Collaboration and mentorship across cross-functional teams (engineering, operations, compliance).&middot; Ownership mindset with a focus on reliability, safety, and continuous improvement.AI Governance, Security &amp; Compliance&middot; Adhere to corporate data governance policies, security standards, and regulatory requirements (e.g., GDPR where applicable).&middot; Implement role-based access controls, encryption, secure secrets management, and audit trails across the ML stack.&middot; Ensure models meet fairness, transparency, and explainability guidelines; conduct regular risk assessments and reviews.Success Criteria &amp; KPIs&middot; Cycle time from ideation to production (weeks).&middot; Model performance against business KPIs (e.g., accuracy, precision/recall, uplift, cost savings).&middot; Operational reliability (SLA adherence, downtime, incident rate).&middot; Adoption and user satisfaction for AI-enabled products and workflows.Compliance metrics (governance checks passed, audits completed).Job Type: Full-timePay: From QAR15,000.00 per monthEducation:<ul><li>Bachelor's (Required)</li></ul>Experience:<ul><li>AI Expert for Plant Data Model (PDM Project) : 3 years (Required)</li></ul>Language:<ul><li>Arabic (Required)</li><li>English (Required)</li></ul>License/Certification:<ul><li>degree in computer science, Data Science (Required)</li></ul>Location:<ul><li>Doha (Required)</li></ul>

AI Expert for Plant Data Model (PDM Project)

<div>
 
 About Commercial Bank Of Qatar
 
 
 
 Commercial Bank, founded in 1975 and headquartered in Doha, plays a vital role in Qatar&rsquo;s economic development by offering a range of personal, business, government, international and investment services.
 
 
 We believe in empowering our employees, providing them with opportunities for growth and professional development.
 
 
 By Joining us, you&rsquo;ll be part of a workplace culture that fosters innovation, supports work-life balance, and encourages you to reach your full potential.
 
 
 Join us in shaping the future of banking!
 
 
 
 Job Summary
 
 
 
 The AI Engineer is responsible for designing, developing, and deploying Agentic AI solutions and AI-enabled platforms within the Data &amp; AI Lab. This role focuses on building robust AI infrastructure including integrations with cloud platforms, vector stores, document processing pipelines, and real-time data streaming capabilities.
 
 
 The position requires strong engineering skills to work with AI orchestration frameworks (MCP), cloud services (Azure, GCP), and modern AI tooling. The AI Engineer will collaborate closely with AI Product Owners, Data Scientists, and other engineers to deliver production-grade AI solutions that support banking operations and customer-facing applications.
 
 
 The role bridges AI research and production deployment, ensuring that AI capabilities are scalable, maintainable, and aligned with enterprise architecture standards.
 
 
 
 
 Key Accountabilities
 
 
 
 
 <ul>
 <li>
 Agentic AI Development
 </li>
 <li>
 Design and implement Agentic AI solutions including autonomous workflows, multi-agent systems, and AI orchestration patterns.
 </li>
 <li>
 Develop and maintain integrations with Model Context Protocol (MCP) and similar AI orchestration frameworks.
 </li>
 <li>
 Build and optimize AI pipelines that combine multiple AI capabilities into cohesive solutions.
 </li>
 <li>
 AI Platform Engineering
 </li>
 <li>
 Develop and maintain AI platform components including vector stores, embedding pipelines, and retrieval systems.
 </li>
 <li>
 Implement document parsing, processing, and metadata extraction pipelines for knowledge management and RAG applications.
 </li>
 <li>
 Build and maintain APIs and integration layers for AI services consumption across banking applications.
 </li>
 <li>
 Cloud &amp; Infrastructure
 </li>
 <li>
 Design and deploy AI solutions on cloud platforms (Azure, GCP) following best practices for scalability and security.
 </li>
 <li>
 Implement data streaming architectures using Kafka for real-time AI applications and event-driven AI workflows.
 </li>
 <li>
 Manage AI infrastructure including model serving on OpenShift/Kubernetes, monitoring, and performance optimization.
 </li>
 <li>
 Integration &amp; Delivery
 </li>
 <li>
 Develop RESTful APIs and integration patterns for AI services consumption by internal and external applications.
 </li>
 <li>
 Collaborate with IT teams to integrate AI capabilities into existing banking systems and workflows.
 </li>
 <li>
 Participate in Agile delivery processes (Scrum, Kanban), contributing to sprint planning, code reviews, and continuous improvement.
 </li>
 <li>
 Quality &amp; Standards
 </li>
 <li>
 Ensure AI solutions comply with the bank's security, governance, and regulatory requirements.
 </li>
 <li>
 Maintain code quality through testing, documentation, CI/CD practices, and adherence to engineering best practices.
 </li>
 <li>
 Stay current with emerging AI technologies, tools, and frameworks to continuously improve delivery capabilities.
 
 
 </li>
 </ul>
 
 Key Competencies
 
 
 
 <ul>
 <li>
 AI &amp; ML Engineering
 </li>
 <li>
 Strong understanding of LLMs, embedding models, and generative AI architectures.
 </li>
 <li>
 Experience with AI orchestration frameworks, agent development, and multi-step AI workflows.
 </li>
 <li>
 Familiarity with vector databases (Azure AI Search, Pinecone, Weaviate, Milvus) and RAG patterns.
 </li>
 <li>
 Knowledge of deep learning frameworks (TensorFlow, PyTorch) and model deployment tools (MLflow, TFX).
 </li>
 <li>
 Software Engineering
 </li>
 <li>
 Python (Advanced level) and relevant AI/ML libraries (LangChain, LlamaIndex, or similar).
 </li>
 <li>
 Experience with API development (REST) and microservices architecture.
 </li>
 <li>
 SQL &ndash; Advanced user (Stored Procedures, Window functions, Temp Tables, Recursive Queries).
 </li>
 <li>
 Git (GitHub/GitLab) for version control and code management.
 </li>
 <li>
 Cloud &amp; Data Engineering
 </li>
 <li>
 Experience with cloud platforms (Azure, GCP) and object storage (S3, GCS, ABS).
 </li>
 <li>
 Knowledge of data streaming technologies (Kafka) and workflow orchestration (Airflow, Apache NiFi).
 </li>
 <li>
 Containerization and orchestration: Docker, K8s (OpenShift).
 </li>
 <li>
 Familiarity with Spark for data processing and MLOps practices.
 </li>
 <li>
 Collaboration
 </li>
 <li>
 Ability to work effectively in cross-functional teams with Data Scientists, ML Engineers, and Product Owners.
 </li>
 <li>
 Strong communication skills to explain technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience working in Agile/Scrum environments (Kanban, Scrum).
 
 
 </li>
 </ul>
 
 Qualifications &amp; Experience
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or related field.
 </li>
 <li>
 4-6 years of experience in software engineering with at least 2 years focused on AI/ML applications.
 </li>
 <li>
 Hands-on experience with cloud platforms (Azure or GCP) and containerization (Docker, OpenShift/K8s).
 </li>
 <li>
 Experience with document processing, metadata extraction, and knowledge management systems preferred.
 </li>
 <li>
 Banking or financial services industry experience is a plus.
 </li>
 <li>
 Relevant certifications (Azure AI Engineer, GCP ML Engineer) preferred.
 
 
 </li>
 </ul>
 
 
 Why Commercial Bank?
 
 
 
 
 <ul>
 <li>
 Best Digital Bank in the Middle East 2024 by World Finance and Best Mobile Banking App in the Middle East 2024 by Global Finance.
 </li>
 <li>
 An Innovation-Driven, Digital-First Environment where employees work with the latest tools and technologies to redefine banking
 </li>
 <li>
 Opportunities for Global Partnerships &amp; International Exposure, connecting employees with global networks and perspectives.
 </li>
 <li>
 A focus on Employee Well-being &amp; Work-Life Balance, ensuring a healthy and supportive environment for all team members
 </li>
 <li>
 Competitive Compensation &amp; Benefits that ensure our employees are rewarded for their dedication and performance
 </li>
 <li>
 A strong Commitment to Diversity, Equity &amp; Inclusion, fostering a culture that values every individual&rsquo;s unique perspective.
 
 
 </li>
 </ul>
 At Commercial Bank, we don&rsquo;t just offer careers, We shape futures by pioneering
 
 digital transformation
 
 in Qatar&rsquo;s banking sector, blending
 
 digital-first
 
 approach to redefine banking through
 
 innovative
 
 solutions.
 
 
 
 Disclaimer
 
 
 
 We appreciate your interest in joining CBQ! Please note that only selected candidates will be contacted for further steps in the hiring process. This job posting is for informational purposes only, and CBQ reserves the right to modify, withdraw, or close it at any time without notice.
</div>

AI Engineer

Milaha

<div>
 
 
 JOB RESPONSIBILITY
 
 
 
 
 
 
 Strategic Leadership &amp; Vision
 
 
 &bull; Define and execute the organization&rsquo;s Data Science &amp; AI strategy in alignment with business goals.
 
 
 &bull; Identify and prioritize opportunities where AI and data driven solutions enhance efficiency, customer experience, product offerings, and revenue generation.
 
 
 &bull; Lead the design of a unified data and AI architecture, including the development of a scalable Data Lakehouse, semantic layers, feature stores, and streaming/batch data integration.
 
 
 &bull; Drive the Generative AI roadmap, selecting LLM providers, designing RAG architectures, and enabling enterprise wide AI capabilities.
 
 
 &bull; Oversee FinOps practices to optimize cloud and compute costs associated with AI, GPU workloads, and large scale data storage.
 
 
 AI &amp; Data Engineering Execution
 
 
 &bull; Oversee the end to end development, validation, deployment, and lifecycle management of machine learning and AI models.
 
 
 &bull; Implement CI/CD/CT pipelines to ensure automated, continuous, and reliable model updates.
 
 
 &bull; Build and maintain vector database infrastructure to support semantic search, contextual AI, and long term knowledge retrieval.
 
 
 &bull; Ensure data pipelines, platforms, and architectures are optimized for performance, scalability, and resilience.
 
 
 Governance, Risk, Compliance &amp; Ethics
 
 
 &bull; Implement data governance, security, and privacy standards, including RBAC/ABAC and protection of PII and sensitive datasets.
 
 
 &bull; Establish and monitor automated controls for model drift, fairness, explainability, and ethical use of AI.
 
 
 &bull; Ensure compliance with relevant regulations (e.g., GDPR, HIPAA) and internal policies.
 
 
 &bull; Maintain best practices for data quality, versioning, reproducibility, and auditability.
 
 
 Innovation &amp; Continuous Improvement
 
 
 &bull; Lead experimentation with new algorithms, methods, and technologies to maintain a cutting edge AI ecosystem.
 
 
 &bull; Promote a culture of continuous improvement, refining processes, tools, and models to enhance performance and business impact.
 
 
 Team Leadership &amp; Capability Building
 
 
 &bull; Mentor and develop a high performing team of data scientists, AI engineers, and data professionals.
 
 
 &bull; Oversee training and upskilling initiatives to ensure the team remains current with emerging technologies and methodologies.
 
 
 Stakeholder Management &amp; Communication
 
 
 &bull; Serve as a strategic advisor between business and technical teams, translating business challenges into AI solutions and articulating AI outcomes clearly to non-technical stakeholders.
 
 
 &bull; Provide regular updates to leadership on progress, performance metrics, model outcomes, and business results.
 
 
 &bull; Drive cross department collaboration to ensure successful adoption and integration of AI solutions.
 
 
 Technology, Tools &amp; Vendor Management
 
 
 &bull; Lead the selection, integration, and management of AI platforms, data tools, and vendor partnerships.
 
 
 &bull; Manage budgets for data science and AI initiatives, ensuring optimal allocation of resources.
 
 
 &bull; Make decisions related to hiring, vendor selection, and solutions procurement that support strategic AI objectives.
 
 
 
 
 
 
 
 
 
 REQUIREMENT
 
 
 
 
 
 
 
 Education &amp; Professional Qualification:
 
 
 
 &bull; Bachelor&rsquo;s Degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology
 
 
 &bull; Master&rsquo;s Degree (preferred):
 
 
 &bull; Data Science, Artificial Intelligence or Machine Learning, Computer Science, Business Analytics, Statistics or Applied Mathematics, Operations Research
 
 
 
 Professional Experience:
 
 
 
 &bull; 6-8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.
 
 
 &bull; Experience with machine learning techniques, such as:
 
 
 - Supervised/Unsupervised Learning
 
 
 - Deep Learning (e.g., Neural Networks, CNNs, RNNs)
 
 
 - Natural Language Processing (NLP)
 
 
 - Computer Vision
 
 
 &bull; Computer Skills:
 
 
 Data science, machine learning, AI, including developing and deploying AI models and algorithms
 
 
 &bull; Language Skills:
 
 
 - Business fluent English. Arabic Language is an advantage.
 
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