sales engineer

logistics manager

project engineer

qa engineer

ai engineer

mlops engineer

DeepSource Technologies

<div>We are looking for a highly capable Senior AI Engineer / MLOps Engineer to join our team and lead the design, development, deployment, and optimization of scalable, production-grade AI and machine learning solutions. The ideal candidate will have strong hands-on experience across AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, with the ability to transform experimentation into reliable, business-ready systems.
This role requires deep expertise in LLMs, RAG, agentic AI workflows, CI/CD automation, production ML lifecycle management, and modern data platforms. The selected candidate will be expected to lead end-to-end AI initiatives, work across multiple projects, collaborate with technical and business stakeholders, and ensure operational excellence across AI platforms.
Key Responsibilities<ul><li>Design, develop, deploy, and maintain production-grade AI and machine learning systems end to end.</li><li>Build and optimize LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI use cases.</li><li>Develop intelligent workflows using tool-calling, orchestration frameworks, and contextual reasoning patterns.</li><li>Fine-tune, evaluate, and operationalize machine learning and foundation models for enterprise use cases.</li><li>Build and manage MLOps pipelines covering training, evaluation, model registration, deployment, monitoring, and retraining.</li><li>Implement CI/CD pipelines for ML and AI workflows to support automated testing, release management, and controlled deployments.</li><li>Establish model monitoring frameworks for drift detection, feature attribution, inference quality, and performance tracking.</li><li>Ensure reproducibility, reliability, and version control across AI/ML environments.</li><li>Architect scalable AI/ML platforms using modern compute, storage, orchestration, monitoring, and search services.</li><li>Build repeatable environments using Infrastructure as Code.</li><li>Support secure, high-availability, and cost-efficient deployment models across development, staging, and production environments.</li><li>Design scalable inference and serving patterns for variable workloads.</li><li>Build and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML consumption.</li><li>Ensure data quality, lineage, governance, and versioning to support dependable model training and inference.</li><li>Work with structured and unstructured datasets across data lakes, data warehouses, and operational systems.</li><li>Deliver analytics-ready datasets to downstream systems and applications.</li><li>Lead multiple AI initiatives in parallel, including planning, execution, and coordination with internal teams and stakeholders.</li><li>Work closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.</li><li>Contribute to architecture decisions, technical documentation, best practices, and engineering standards.</li><li>Support knowledge sharing, technical leadership, and continuous improvement across the AI function. 
</li></ul> 
 
Requirements<ul><li> 
Bachelor&rsquo;s degree in Computer Engineering, Computer Science, Artificial Intelligence, Data Science, or a related field.</li><li>5+ years of hands-on experience in AI engineering, machine learning engineering, MLOps, or data/ML platform engineering.</li><li>Proven experience deploying production AI/ML solutions in enterprise environments.</li><li>Strong programming experience in Python and SQL.</li><li>Strong experience with enterprise AI/ML architecture and delivery.</li></ul></div>

Senior Ai Engineer - Riyadh,KSA

Master Works

<div>
 This is a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. This role sits at the intersection of data science, software engineering, and MLOps, and requires strong hands-on experience turning models into production-ready solutions, programming experience in Python or R.
 
 
 <ul>
 <li>
 Design, develop, train, and optimize machine learning models for real applications or use cases
 </li>
 <li>
 Translate business and product requirements into scalable ML/AI solutions.
 </li>
 <li>
 Implement feature engineering, model selection, tuning, and evaluation techniques.
 </li>
 <li>
 Develop , and deploy ML models into production environments with high availability and performance
 </li>
 <li>
 Build and maintain ML pipelines (training, validation, deployment, monitoring).
 </li>
 <li>
 Monitor model performance, data drift, and model decay; retrain models as needed.
 </li>
 <li>
 Ensure models meet reliability, scalability, and security standards.
 </li>
 <li>
 Work closely with Data Scientists, Product Managers, and Software Engineers.
 </li>
 <li>
 Collaborate with data engineering teams to ensure high-quality, reliable data pipelines
 </li>
 <li>
 Participate in design and code reviews, ensuring engineering best practices.
 </li>
 <li>
 Optimize models for latency, throughput, and cost.
 </li>
 <li>
 Implement experimentation frameworks (A/B testing, offline evaluation).
 </li>
 <li>
 Apply responsible AI principles, including fairness, explainability, and governance where required
 
 
 </li>
 </ul>
 
 Requirements
 
 
 
 <ul>
 <li>
 3-7+ years of hands-on experience in Machine Learning or applied AI roles
 </li>
 <li>
 Strong programming skills in Python (and/or Java, Scala).
 </li>
 <li>
 Solid understanding of ML algorithms (supervised, unsupervised, deep learning).
 </li>
 <li>
 Experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn.
 </li>
 <li>
 Experience deploying models using Docker, Kubernetes, or cloud ML services.
 </li>
 <li>
 Strong knowledge of data structures, algorithms, and software engineering principles.
 </li>
 <li>
 Experience working in agile, cross-functional teams
 </li>
 <li>
 Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.
 </li>
 <li>
 Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
 </li>
 <li>
 Experience with big data technologies (Spark, Kafka, Databricks).
 </li>
 <li>
 Background in NLP, Computer Vision, or Generative AI.
 </li>
 <li>
 Strong problem-solving and analytical thinking
 </li>
 <li>
 Production-first mindset
 </li>
 <li>
 Data-driven decision making
 </li>
 <li>
 High Collaboration and communication skills
 </li>
 </ul>
</div>

AI Engineer

MASTER-WORKS

<div>This is a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. This role sits at the intersection of data science, software engineering, and MLOps, and requires strong hands-on experience turning models into production-ready solutions, programming experience in Python or R.
<ul><li>Design, develop, train, and optimize machine learning models for real applications or use cases.</li><li>
Translate business and product requirements into scalable ML/AI solutions.
</li><li>Implement feature engineering, model selection, tuning, and evaluation techniques.
</li><li>Develop , and deploy ML models into production environments with high availability and performance.</li><li>
Build and maintain ML pipelines (training, validation, deployment, monitoring).
</li><li>Monitor model performance, data drift, and model decay; retrain models as needed.
</li><li>Ensure models meet reliability, scalability, and security standards.
</li><li>Work closely with Data Scientists, Product Managers, and Software Engineers.
</li><li>Collaborate with data engineering teams to ensure high-quality, reliable data pipelines.</li><li>
Participate in design and code reviews, ensuring engineering best practices.
</li><li>Optimize models for latency, throughput, and cost.
</li><li>Implement experimentation frameworks (A/B testing, offline evaluation).
</li><li>Apply responsible AI principles, including fairness, explainability, and governance where required.</li></ul> 

Requirements 
<ul><li>
3&ndash;7+ years of hands-on experience in Machine Learning or applied AI roles.</li><li>
Strong programming skills in Python (and/or Java, Scala).
</li><li>Solid understanding of ML algorithms (supervised, unsupervised, deep learning).
</li><li>Experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn.
</li><li>Experience deploying models using Docker, Kubernetes, or cloud ML services.
</li><li>Strong knowledge of data structures, algorithms, and software engineering principles.
</li><li>Experience working in agile, cross-functional teams.</li><li>
Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.
</li><li>Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
</li><li>Experience with big data technologies (Spark, Kafka, Databricks).
</li><li>Background in NLP, Computer Vision, or Generative AI.
</li><li>Strong problem-solving and analytical thinking
</li><li>Production-first mindset
</li><li>Data-driven decision making
</li><li>High Collaboration and communication skills</li></ul></div>

<div>Design, develop, and deploy Arabic-first Large Language Model (LLM) solutions to support AI-driven products and client projects, ensuring high-quality Arabic language understanding, generation, and contextual accuracy across enterprises.
Key Responsibilities1. LLM Development &amp; NLP (Core)<ul><li>
Design and fine-tune LLMs for Arabic language tasks
</li><li><ul><li>Text generation, classification, summarization
</li><li>Arabic grammar correction, dialect handling
</li></ul></li><li>Build and optimize RAG pipelines
</li><li>Develop prompt engineering strategies for Arabic + English contexts
</li></ul>2. Data &amp; Arabic Language Engineering<ul><li>
Build and curate Arabic datasets
</li><li>Handle:
</li><li><ul><li>Dialects (Gulf, MSA, etc.)
</li><li>Data cleaning, normalization
</li></ul></li><li>Define evaluation benchmarks for Arabic LLM quality
</li></ul>3. AI System Architecture<ul><li>
Develop LLM-powered systems using:
</li><li><ul><li>LangChain / LangGraph / Llama / OpenAI APIs
</li><li>Vector databases (ChromaDB, FAISS, etc.)
</li></ul></li><li>Integrate LLMs into:
</li><li><ul><li>APIs
</li><li>Enterprise systems
</li><li>AI agents / copilots
</li></ul></li></ul>4. Production &amp; MLOps<ul><li>
Deploy scalable AI systems using:
</li><li><ul><li>Docker, Kubernetes
</li><li>Cloud platforms (Azure, AWS, GCP)
</li></ul></li><li>Optimize:
</li><li><ul><li>Latency
</li><li>Cost
</li><li>Throughput
</li></ul></li></ul>5. Cross-functional Collaboration<ul><li>
Work with:
</li><li><ul><li>PMs (scope, delivery, timelines)
</li><li>BAs (requirements translation AI logic)
</li><li>QA (model validation &amp; test cases)
</li></ul></li><li>Support client demos and AI solution design
</li></ul>6. Governance Alignment (Critical for Nabeh)<ul><li>
Ensure:
</li><li><ul><li>AI outputs align with client expectations
</li><li>Traceability (data model output)
</li></ul></li><li>Support:
</li><li><ul><li>BRD validation (AI feasibility)
</li><li>UAT and acceptance criteria</li></ul></li></ul> 

Requirements 
 
Required SkillsTechnical<ul><li>
Strong in:
</li><li><ul><li>Python (PyTorch / TensorFlow)
</li><li>NLP &amp; LLMs (Transformers, RAG)
</li></ul></li><li>Experience with:
</li><li><ul><li>LangChain / LLM frameworks
</li><li>Vector databases
</li><li>Prompt engineering
</li></ul></li></ul>Arabic AI (Mandatory)<ul><li>
Native or fluent Arabic
</li><li>Experience in:
</li><li><ul><li>Arabic NLP
</li><li>Dataset preparation for Arabic
</li><li>Handling dialects
</li></ul></li></ul>Engineering &amp; Deployment<ul><li>
APIs (FastAPI / Flask)
</li><li>Microservices architecture
</li><li>Docker, Kubernetes
</li><li>CI/CD pipelines</li></ul>
Language Requirements<ul><li>
Arabic: Native / Fluent (MANDATORY)
</li><li>English: Professional (MANDATORY)
</li></ul>Preferred Qualifications<ul><li>
MSc or higher in AI / Data Science / NLP
</li><li>Experience in:
</li><li><ul><li>Arabic LLMs (high priority)
</li><li>Government or enterprise AI projects
</li></ul></li><li>Certifications:
</li><li><ul><li>Azure AI / ML
</li><li>MLOps / Cloud certifications</li></ul></li></ul></div>

LLM Engineer - Arabic Speaker

<div><div>We help the world run better 
At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging &ndash; but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.</div> 
What you&rsquo;ll build 
In this role, you'll lead the design and delivery of production-grade ML and generative AI systems that solve complex product and business problems at scale. You'll own architecture and implementation across data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation, and observability.
You'll drive advanced use cases across deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and LLM applications, including RAG, tool use, evaluation harnesses, and safety controls. Your work will directly shape performance, reliability, latency, and cost efficiency in live environments. You&rsquo;ll guide technical direction on topics such as model selection, distributed training and inference, GPU utilization, model compression, prompt and retrieval optimization, drift detection, retraining strategy, and responsible AI controls. You&rsquo;ll mentor other engineers and turn best practices into reusable patterns and platform capabilities. 
What you bring: 
<ul><li>
You bring expert-level programming skills in Python, along with strong software engineering expertise in languages such as Java or Go, enabling you to build scalable, production-grade systems</li><li>
You have deep knowledge of machine learning, deep learning, and optimization techniques across structured data, NLP, search, ranking, and recommendation problems</li><li>
You have extensive experience designing and operating end-to-end ML systems, from data ingestion and experimentation to deployment, observability, and lifecycle management</li><li>
You bring strong hands-on experience with modern ML and LLM tooling (e.g., PyTorch, TensorFlow, scikit-learn), including fine-tuning, evaluation, orchestration, and model serving</li><li>
You have practical experience building generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and guardrails</li><li>
You bring deep expertise in MLOps and platform engineering, including model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, and automated validation</li><li>
You have a strong architectural understanding of distributed systems, event-driven services, streaming data, and cloud-native ML platforms, with the ability to optimize for performance, scalability, reliability, and cost</li><li>
You define robust evaluation and governance strategies, including offline benchmarking, online experimentation, hallucination analysis, model risk assessment, and responsible AI practices</li></ul> 
About You<ul><li>
You bring 7-9+ years of experience in software engineering and machine learning, with a track record of leading and delivering complex, production-scale AI systems</li><li>
You combine strong product judgment with technical depth, translating ambiguous business problems into scalable, high-impact AI solutions</li><li>
You thrive in complex, fast-moving environments and bring clarity, ownership, and strategic thinking to drive long-term platform success</li></ul> 
Where you belong: 
You will be part of a growing team of AI and industry experts dedicated to serving customers across the Kingdom of Saudi Arabia / United Arab Emirates. We are building a collaborative, high-impact environment that brings together deep AI expertise, strong industry knowledge, and regional understanding to help customers innovate, transform, and lead in their markets.
This team thrives on working together to turn complex business challenges into practical, scalable solutions. You will find an environment that values curiosity, ownership, and continuous learning, where new ideas are encouraged and real-world impact matters. This is a place for people who enjoy building something new. You will thrive here if you are customer-centric, motivated by meaningful outcomes, and comfortable navigating ambiguity in a fast-evolving AI landscape. 
About the team
We are a team of engineers passionate about building valuable, scalable AI frameworks that drive real business impact. We thrive on solving challenging problems, innovating
with cutting-edge technologies, and collaborating closely to deliver AI systems that are reliable, high-performing, and ready for production. Our team values ownership, technical excellence, and creating solutions that stand the test of scale and complexity.<div> 
Bring out your best 
SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.
 
 
We win with inclusion 
SAP&rsquo;s culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone &ndash; regardless of background &ndash; feels included and can run at their best. At SAP, we believe we are made stronger by the unique capabilities and qualities that each person brings to our company, and we invest in our employees to inspire confidence and help everyone realize their full potential. We ultimately believe in unleashing all talent and creating a better world. 
 
SAP is committed to the values of Equal Employment Opportunity and provides accessibility accommodations to applicants with physical and/or mental disabilities. If you are interested in applying for employment with SAP and are in need of accommodation or special assistance to navigate our website or to complete your application, please send an e-mail with your request to Recruiting Operations Team: Careers@sap.com. 
 
For SAP employees: Only permanent roles are eligible for the SAP Employee Referral Program, according to the eligibility rules set in the SAP Referral Policy. Specific conditions may apply for roles in Vocational Training. 
 
Qualified applicants will receive consideration for employment without regard to their age, race, religion, national origin, ethnicity, gender (including pregnancy, childbirth, et al), sexual orientation, gender identity or expression, protected veteran status, or disability, in compliance with applicable federal, state, and local legal requirements. 
 
Successful candidates might be required to undergo a background verification with an external vendor.</div><div></div><div> 
AI Usage in the Recruitment Process</div><div>
For information on the responsible use of AI in our recruitment process, please refer to our Guidelines for Ethical Usage of AI in the Recruiting Process.
</div>Please note that any violation of these guidelines may result in disqualification from the hiring process. 
 
Requisition ID: 450218 | Work Area: Software-Design and Development | Expected Travel: 0 - 10% | Career Status: Professional | Employment Type: Regular Full Time | Additional Locations: #LI-Hybrid</div>

Senior Machine Learning Engineer

Gathern

<div>Job Purpose:
The Business Intelligence Manager owns the company&rsquo;s Business Intelligence function and is accountable for turning data into trusted insights that drive strategic and operational decisions. This role leads the activation of BI across the organization, focusing on correlation, causation, forecasting, and storytelling.The role also leads applied AI and machine learning initiatives, managing ML engineers to build, deploy, and operate production-grade, business-driven models, with clear ownership of applied MLOps practices to ensure reliability, scalability, and trust.
Key Responsibilities<ul><li>
 Lead the BI team in planning, developing, and delivering high-impact dashboards, reports, and analytics solutions</li><li>
 Business Intelligence Leadership</li></ul><ul><li>
Own the end-to-end Business Intelligence strategy and execution</li><li>
Establish BI as a core decision-support capability across the company</li><li>
Define and govern KPIs, metrics, and analytical standards</li><li>
Ensure insights consistently explain what happened, why it happened, and what to do next</li><li>
Lead executive and operational reporting and insight narratives</li></ul>
Analytics, Forecasting &amp; Insights<ul><li>
Oversee trend analysis, root-cause analysis, and correlation studies</li><li>
Drive forecasting, projections, and scenario analysis for planning and expectation-setting</li><li>
Ensure analytical outputs are explainable, assumption-driven, and decision-ready</li></ul>
Applied AI and Machine Learning Leadership<ul><li>
Lead and mentor ML engineers to design, build, and deploy applied machine learning models</li><li>
Own the applied ML and MLOps roadmap, aligned with BI and business priorities</li><li>
Guide the development of models such as:</li><li><ul><li>
Recommendation and pattern discovery models</li><li>
Anomaly detection</li></ul></li><li>
Define and enforce MLOps standards, including:</li><li>
Model versioning and lifecycle management</li><li>
Deployment and rollback strategies</li><li>
Monitoring model performance, drift, and data quality</li><li>
Retraining and validation processes</li><li>
Ensure ML models are:</li><li>
Business-driven and use-case oriented</li><li>
Explainable and interpretable</li><li>
Observable and maintainable in production</li><li>
Cross-Functional Leadership</li><li>
Act as the intelligence partner for Product, Finance, Operations, and Engineering</li><li>
Work closely with Data Engineering, Backend Engineering, and Platform teams to operationalize insights and ML models</li></ul>
Technical &amp; Analytical Requirements<ul><li>
Strong understanding of Business Intelligence and analytics practices</li><li>
Hands-on experience with BI tools (Power BI preferred)</li><li>
Strong analytical foundation, including:</li><li><ul><li>
Correlation vs causation</li><li>
Forecasting and scenario modeling</li><li>
Root-cause analysis</li></ul></li><li>
Solid understanding of data warehousing concepts (BigQuery preferred)</li><li>
Working knowledge of applied machine learning, including:</li><li><ul><li>
Supervised and unsupervised learning approaches</li><li>
Feature engineering and model evaluation</li><li>
Model explainability techniques</li></ul></li><li>
Working knowledge of MLOps concepts, including:</li><li><ul><li>
Model deployment and lifecycle management</li><li>
Monitoring for performance degradation and data drift</li><li>
Retraining strategies and validation workflows</li><li>
Collaboration with engineering teams on CI/CD and production readiness</li></ul></li><li>
Ability to guide ML engineers on both modeling and operationalization, without acting as a research data scientist</li></ul> 

Requirements 
<ul><li>
Bachelor&rsquo;s degree in Computer Science, Information Systems, or a related field..</li><li>
5+ years of experience in business intelligence, data analytics, or data management.</li><li>
Applied ML models are deployed, monitored, and reliably operated in production</li><li>
MLOps practices ensure models remain accurate, observable, and trusted over time</li><li>
ML outputs are explainable and confidently used by business stakeholders</li><li>
BI remains the starting point for decisions, with ML and MLOps extending insight into action</li></ul></div>

BI Manager

AIQU

<div>
 
 
 About the Company
 
 
 
 
 
 
 
 
 
 We are seeking a highly skilled Senior Data Scientist to lead the design, development, and deployment of advanced autonomous agents and Large Language Models (LLMs). This role offers the chance to drive innovative AI solutions in a collaborative, dynamic environment, working on cutting-edge systems that solve real-world challenges.
 
 
 
 
 
 
 
 
 
 About the Role
 
 
 
 
 
 
 
 
 
 We are seeking a highly skilled Senior Data Scientist to lead the design, development, and deployment of advanced autonomous agents and Large Language Models (LLMs). This role offers the chance to drive innovative AI solutions in a collaborative, dynamic environment, working on cutting-edge systems that solve real-world challenges.
 
 
 
 
 
 
 
 
 
 Responsibilities
 
 
 
 
 
 
 
 
 <ul>
 <li>
 Agent Development: Design and implement autonomous agents that perform tasks, call external tools/APIs, and generate code to solve complex problems.
 </li>
 <li>
 LLM Integration: Develop and integrate Large Language Models to enhance agent capabilities in decision-making and natural language processing.
 </li>
 <li>
 Tooling: Equip agents with the right toolchains&mdash;RAG, vector DBs, third-party APIs&mdash;to streamline task execution.
 </li>
 <li>
 Safety &amp; Ethics: Incorporate guardrails, alignment tests, and compliance standards to ensure secure, reliable operation.
 </li>
 <li>
 Performance Optimisation: Use MLOps/DevOps best practices to monitor, benchmark, and refine models for cost-efficient scale.
 </li>
 <li>
 Collaboration: Partner with cross-functional teams to align technical solutions with business goals.
 </li>
 <li>
 Performance Optimization: Refine agent and LLM performance using MLOps and DevOps best practices.
 </li>
 <li>
 Deployment: Own end-to-end rollout on cloud (AWS, Azure, or GCP), containerising with Docker/Kubernetes where appropriate.
 </li>
 <li>
 Cross-functional Alignment: Work closely with product, engineering, and domain SMEs to translate business goals into technical roadmaps.
 </li>
 <li>
 Mentorship: Coach and review work of junior data scientists/ML engineers.
 </li>
 <li>
 Innovation: Dedicate a portion of your time to tracking AI trends and prototyping new autonomous-agent capabilities.
 </li>
 <li>
 Communication: Present insights and outcomes clearly to both technical and non-technical stakeholders.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Qualifications
 
 
 
 
 
 
 
 
 <ul>
 <li>
 Master&rsquo;s or Ph.D. in Computer Science, Artificial Intelligence, or related field (or equivalent practical experience).
 </li>
 <li>
 7 + years in data science/ML engineering, with 4 + years building AI systems that include autonomous agents or LLMs.
 </li>
 <li>
 Proven record of deploying agentic systems with integrated safety features.
 </li>
 <li>
 Expert-level Python and deep familiarity with PyTorch or TensorFlow.
 </li>
 <li>
 Hands-on experience running production workloads on at least one major cloud platform.
 </li>
 <li>
 Solid grasp of MLOps (CI/CD, model monitoring, IaC).
 </li>
 <li>
 Strong problem-solving ability and clear, concise communication skills.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Required Skills
 
 
 
 
 
 
 
 
 <ul>
 <li>
 Expert-level Python and deep familiarity with PyTorch or TensorFlow.
 </li>
 <li>
 Solid grasp of MLOps (CI/CD, model monitoring, IaC).
 </li>
 <li>
 Strong problem-solving ability and clear, concise communication skills.
 </li>
 </ul>
 
 
 
 
 
 
 
 
 Preferred Skills
 
 
 
 
 
 
 
 
 <ul>
 <li>
 Hands-on experience running production workloads on at least one major cloud platform.
 </li>
 <li>
 Proven record of deploying agentic systems with integrated safety features.
 </li>
 </ul>
 
 
</div>

Senior Data Scientist - Autonomous Agents and LLMs

Explore 222+ Mlops engineer in Riyadh this April. Find roles across top companies and apply now to boost your career.

222 mlops engineer jobs In Riyadh

Senior Ai Engineer - Riyadh,KSA

AI Engineer

AI Engineer

LLM Engineer - Arabic Speaker

Senior Machine Learning Engineer

BI Manager

Senior Data Scientist - Autonomous Agents and LLMs