Apply for Senior Data Scientist Job in Qatar Specializing in AI

Why This Role Matters in 2025

Artificial Intelligence is no longer a futuristic buzzword—it’s the backbone of modern innovation. From chatbots that answer your banking questions to AI-powered tools that automate entire business workflows, AI is everywhere. In 2025, companies are no longer just looking for “data scientists”; they want professionals who can handle next-generation AI systems, particularly agentic AI and Large Language Models (LLMs).

This job isn’t just about crunching numbers or fine-tuning models—it’s about creating intelligent agents that think, reason, and act autonomously. That’s why the Senior Data Scientist role is a game-changer. It blends deep machine learning knowledge with production-grade deployment skills, ensuring businesses can actually use these cutting-edge models in real-world scenarios.

The Growing Importance of Agentic AI and LLMs

Think of agentic AI as the evolution of chatbots. Instead of simply answering questions, these systems can make decisions, collaborate with other agents, and complete tasks end-to-end. Pair that with the power of LLMs like GPT and Claude, and you’ve got tools that can write, analyze, summarize, plan, and even create strategies.

Businesses are rushing to adopt this tech because it saves time, reduces costs, and boosts productivity. But here’s the catch: building such systems requires a blend of AI research, software engineering, and MLOps expertise. That’s where this role shines—it’s not just about experimenting in a lab; it’s about delivering real-world AI solutions that work at scale.

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Company Overview – Ghaia.ai

Who is Ghaia.ai?

Ghaia.ai is a forward-thinking AI company focused on building enterprise-grade autonomous systems. While many organizations are still experimenting with generative AI, Ghaia.ai is already creating full-fledged AI agents and multi-agent orchestration frameworks. This means they’re at the cutting edge of where AI is headed.

The company operates in Doha, Qatar, but it also offers flexibility with remote roles. This gives professionals across the globe an opportunity to contribute to groundbreaking AI projects without being tied to one location.

Vision and Mission of the Company

The vision of Ghaia.ai is simple yet ambitious: to transform businesses using intelligent AI agents. Their mission revolves around combining innovation, scalability, and real-world usability. Instead of creating flashy demos, they’re focused on deploying solutions that can stand the test of enterprise needs—whether that’s in finance, healthcare, logistics, or other industries.

By prioritizing agentic AI and MLOps excellence, they aim to bridge the gap between AI research and business implementation.

Work Culture and Team Structure

Ghaia.ai is a relatively small team, but don’t let that fool you. Smaller teams often mean more responsibility, faster decision-making, and greater visibility for your contributions. Employees are expected to wear multiple hats—ranging from AI research to collaboration with engineers.

This culture is perfect for professionals who thrive in dynamic, startup-like environments where innovation is valued more than bureaucracy. If you enjoy working closely with architects, product managers, and engineers to bring AI ideas to life, this company could be the ideal fit.

Key Responsibilities of the Role

Designing Autonomous AI Agents

One of the most exciting responsibilities of this role is creating autonomous AI agents. Unlike traditional machine learning models that simply output predictions, these agents are designed to take action, interact with users, and even collaborate with other agents. Think of it like building a digital workforce—AI systems that can perform tasks just like human employees but faster and more efficiently.

You’ll be expected to design architectures that allow these agents to reason, plan, and execute tasks in real time. This involves deep knowledge of frameworks like LangGraph, CrewAI, and AutoGen, which specialize in agent-based orchestration.

Building Multi-Agent Orchestration Frameworks

Sometimes, one AI agent isn’t enough. Businesses need a network of agents that can work together seamlessly. For example, one agent might pull financial data, another might generate insights, and a third could draft a report for the management team.

As a Senior Data Scientist, your job will be to build these multi-agent frameworks, ensuring they communicate effectively, delegate tasks, and avoid conflicts. This is a cutting-edge responsibility because few companies are even experimenting at this level, which means your work directly shapes the future of AI automation.

Working with Large Language Models (LLMs)

At the heart of this role are LLMs like GPT, Claude, and LLaMA. You’ll be fine-tuning them, integrating them into applications, and extending their abilities beyond text generation. The focus will be on summarization, classification, question answering, and dialogue systems.

The challenge here is not just about making LLMs smart, but also making them useful for specific industries. For example, a banking LLM must understand compliance terms, while a healthcare LLM should be medically accurate.

Implementing Retrieval-Augmented Generation (RAG)

LLMs are powerful, but they don’t always have up-to-date knowledge. That’s where RAG (Retrieval-Augmented Generation) comes in. You’ll be working with vector databases, embeddings, and search pipelines to give LLMs access to the latest data.

Think of RAG as giving your AI a library card—it ensures the model isn’t limited to what it learned during training but can continuously fetch new, relevant information to answer queries.

Applying Traditional Machine Learning Models

Not everything is about LLMs. Many industries still rely on traditional ML models for forecasting, classification, and regression. In this role, you’ll balance both worlds—applying deep learning where necessary but also ensuring traditional models are optimized for accuracy, interpretability, and speed.

End-to-End Model Lifecycle Management

Building an AI model isn’t enough—you need to ensure it thrives in the wild. You’ll be handling the entire lifecycle: from data preparation and model training to deployment, monitoring, and retraining. This responsibility ensures that models don’t just work once but continue delivering value over time.

MLOps and Deployment Responsibilities

Experimentation and Prototyping

Before anything goes live, it starts with experimentation. You’ll be running proof-of-concept projects, A/B tests, and rapid prototypes. The goal here is to move fast, test ideas, and identify which approaches deliver the most impact without wasting resources.

Think of this phase as the “sandbox” where creativity and testing collide. Strong documentation and experiment tracking will be essential here.

Production Deployment and CI/CD

The real challenge in AI isn’t just building a good model—it’s deploying it into production where it can serve thousands of users. That’s why CI/CD (Continuous Integration and Continuous Deployment) is at the heart of this role.

You’ll need to automate model deployment pipelines, containerize applications (using Docker or Kubernetes), and ensure smooth integration with enterprise systems. Every update must be fast, reliable, and without downtime.

Monitoring and Continuous Model Improvement

AI models degrade over time due to data drift, concept drift, and changing business needs. Your job will include setting up monitoring systems that track performance, detect anomalies, and trigger retraining processes when needed.

This is where the role truly shifts from “data scientist” to AI operations leader. You’ll make sure models are not just deployed but also sustainable, scalable, and continuously improving.

Technical Skills Required

LLMs and Generative AI Expertise

This role is all about pushing the boundaries of what LLMs can do. You’ll need strong hands-on experience with fine-tuning, prompt engineering, and leveraging APIs from models like GPT, Claude, LLaMA, and Falcon. Beyond that, you’ll be expected to customize LLMs for enterprise applications—not just use them out of the box.

If you’ve worked on summarization tools, intelligent assistants, or conversational AI, that’s a huge plus. The key here is to show you can transform general-purpose LLMs into specialized business problem-solvers.

MLOps Tools and Best Practices

MLOps is the backbone of this role. You’ll need deep familiarity with tools that support automation, monitoring, and scaling AI models. Think of platforms like:

MLflow for experiment tracking
Kubeflow or Airflow for orchestration
Docker and Kubernetes for containerization
CI/CD pipelines for continuous delivery

It’s not enough to train a model—you’ll need to prove you can keep it alive and well in production for months or even years.

Cloud Experience – Microsoft Azure Stack

This role is heavily tied to Azure AI services. You should be comfortable with:

Azure Machine Learning for model training and deployment
Azure Cognitive Services for NLP and vision APIs
Azure Synapse for data integration and analytics
Azure Kubernetes Service (AKS) for large-scale deployments

Even if you’ve worked with AWS or GCP before, the ability to quickly adapt to Azure’s ecosystem is essential. Azure is becoming a major player in enterprise AI adoption, and this job puts you right at the center of that growth.

Strong Machine Learning Foundations

While LLMs are trendy, companies still need strong classical ML expertise. You should be comfortable with:

Regression and classification models
Time series forecasting
Clustering and recommendation systems
Model explainability and fairness

In other words, you need to be as comfortable building a credit risk model as you are fine-tuning an LLM. This balance will set you apart from candidates who only know one side of AI.

Collaboration and Leadership Duties

Working with Engineers and Architects

AI doesn’t live in isolation—it must integrate with real-world systems. That’s why you’ll work closely with software engineers, data engineers, and solution architects. You’ll need to speak both “data science” and “engineering,” ensuring your models can be scaled and integrated seamlessly.

For example, you might design a forecasting model, but the engineers will integrate it into an app used by thousands of customers. That collaboration is what turns your work into tangible impact.

Translating Business Needs into AI Solutions

Not every stakeholder understands AI jargon. Part of your role will be to bridge the gap between business and technology. This means listening to business leaders, understanding their pain points, and translating those into AI solutions.

For instance, if a retailer struggles with stockouts, you might design a predictive inventory model. If a bank wants better customer support, you might build an agentic AI chatbot.

This ability to speak both business and technical language is what makes you more than just a data scientist—you become a strategic problem solver.

Mentoring Junior Data Scientists

As a senior role, leadership is key. You’ll be expected to mentor junior team members, review their work, and guide them in adopting best practices. Think of yourself as both a builder and a teacher.

This is also your chance to influence the culture of the team, ensuring that Ghaia.ai maintains high standards in AI research, engineering, and ethics. Mentorship not only helps others—it also positions you as a leader and future decision-maker.

Job Requirements and Qualifications

Years of Experience and Industry Background

To step into this role, you’ll need at least five years of hands-on experience in data science, machine learning, or AI engineering. But it’s not just about the number of years—it’s about the depth of your experience. Employers will be looking for candidates who have already deployed models in production, not just played around with them in research settings.

If your background includes enterprise projects, AI integrations, or end-to-end lifecycle management, you’ll stand out. Experience in industries like finance, healthcare, or logistics can also give you an edge, since these are common sectors for AI adoption.

Education and Academic Requirements

A Bachelor’s degree in computer science, data science, or a related field is typically the minimum. However, a Master’s or PhD in AI, machine learning, or computational fields is often preferred, especially for senior-level positions.

That said, many companies now also value practical skills and portfolios just as much as formal degrees. A candidate with solid hands-on experience, open-source contributions, or published AI projects can sometimes outshine someone with advanced degrees but little practical exposure.

Preferred Technical Certifications

Certifications aren’t always mandatory, but they can definitely strengthen your application. Since this role is heavily tied to Azure, certifications like:

Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure Data Scientist Associate
Azure Solutions Architect Expert

will make you look much more competitive. Other relevant certs might include TensorFlow Developer Certificate or AWS/GCP AI/ML certifications, showing versatility across cloud environments.

Challenges of the Role

Keeping Up with Fast-Evolving AI Trends

AI is moving at lightning speed. New frameworks, models, and methods emerge almost every month. As a Senior Data Scientist, you’ll be expected to stay ahead of the curve. That means continuous learning, experimenting with new tools, and quickly adapting to industry shifts.

Imagine it like surfing—you can’t afford to miss the next big wave in AI, or you risk becoming outdated.

Balancing Research and Production Demands

This role requires walking a fine line between cutting-edge research and business practicality. You might want to try the latest LLM model or experiment with a new agentic framework, but stakeholders will be focused on stability, cost-effectiveness, and reliability.

Finding that balance—between innovation and real-world application—is one of the toughest but most rewarding aspects of the job.

Adapting to a Startup-Like Work Environment

Ghaia.ai operates more like a startup than a large enterprise. That means things move fast, responsibilities overlap, and processes may not always be structured.

For some, this is thrilling—every day feels different, and you have the freedom to shape projects. But for others used to strict corporate processes, it can feel chaotic. Success here depends on being flexible, resourceful, and proactive.

How to Prepare for This Job

Building LLM and Agentic AI Skills

If you’re aiming for this role, you can’t just know the basics of AI—you need to be fluent in LLMs and agentic frameworks. Start by experimenting with open-source models like LLaMA 2, Falcon, or Mistral. Play with frameworks such as LangChain, CrewAI, and AutoGen, and build small projects like chatbots, AI planners, or document summarizers.

Think of it like training for a marathon—you don’t just show up on race day. You prepare by running smaller distances, building stamina, and testing your limits. Similarly, the more you tinker with LLMs and agents, the better equipped you’ll be for real-world enterprise projects.

Mastering MLOps with Azure Tools

Since this role emphasizes Azure, you’ll want to get hands-on with Microsoft’s AI ecosystem. Try deploying a simple model using Azure ML, practice setting up pipelines with Azure DevOps, and explore Cognitive Services for NLP and vision tasks.

Even if you’re more comfortable with AWS or GCP, having Azure certifications or projects on your resume can give you an edge. Employers want proof that you can step in and be productive from day one.

Strengthening Deployment and Monitoring Expertise

Building a model is like baking a cake—it’s fun and creative. But deploying and monitoring it is like running a bakery—you need consistency, reliability, and scalability.

Focus on learning:

Docker & Kubernetes for containerization
Monitoring tools like Prometheus, Grafana, or built-in Azure dashboards
Automated retraining workflows to handle data drift

This will prove that you’re not just a researcher but someone who can run AI systems in production at scale.

Continuous Learning and Networking

Finally, don’t underestimate the power of staying connected. Attend AI conferences, webinars, and workshops, and engage in online communities like Reddit ML, Kaggle, or GitHub. Networking not only exposes you to new tools and trends but can also open doors to career opportunities—sometimes faster than job boards.

Salary Expectations in Qatar and Remote

Typical Salary Range for Senior Data Scientists

In Qatar, a Senior Data Scientist role typically pays between QAR 300,000 to QAR 450,000 annually (roughly USD $82,000 – $123,000). Salaries may vary depending on your experience, specialization in AI/LLMs, and whether you’re working onsite or remotely.

For remote positions, salaries might align more with international benchmarks, ranging from $100,000 to $140,000 annually, especially if you bring rare expertise in LLMs, MLOps, and Azure AI.

Comparison with Other AI Roles in the Region

Compared to other roles in the Middle East:

Standard Data Scientist: QAR 220,000 – 300,000 annually
Machine Learning Engineer: QAR 250,000 – 350,000 annually
Senior AI Engineer (specialized in LLMs/agents): QAR 350,000 – 500,000 annually

Clearly, this Senior Data Scientist role is at the top end of the salary range. The demand for LLM and agentic AI expertise is skyrocketing, which makes this position not only financially rewarding but also highly strategic for your career.

Apply Now – How to Land the Job

If you’re ready to take your career to the next level with one of the most exciting AI roles in the market, now’s your chance. Tailor your resume to highlight your LLM, MLOps, and Azure expertise, and be sure to showcase any hands-on projects with agentic AI frameworks. Strong applications emphasize both technical depth and real-world impact.

👉 You can apply directly here: Apply for Senior Data Scientist at Ghaia.ai

Conclusion

The Senior Data Scientist (Agentic AI, LLMs & MLOps) role at Ghaia.ai is not your typical data science job. It’s an opportunity to shape the future of AI by working on autonomous agents, enterprise-ready LLMs, and scalable MLOps solutions. This position combines cutting-edge research with real-world application, making it perfect for professionals who want to be at the forefront of AI innovation.

Yes, the role comes with challenges—like keeping up with AI’s rapid evolution and managing startup-style dynamics—but the rewards are immense. From competitive salaries to hands-on exposure to the latest AI tools, this job is a stepping stone to becoming a global AI leader. If you’re passionate about AI, innovation, and impact, this might just be the career-defining role you’ve been waiting for.

FAQs

What makes this job unique compared to other AI roles?

It focuses specifically on agentic AI and LLMs, which are among the fastest-growing fields in artificial intelligence, blending research with enterprise deployment.

Do I need prior Azure experience to apply?

Yes, Azure experience is strongly preferred since Ghaia.ai operates primarily on the Microsoft Azure ecosystem. However, strong cloud skills in AWS/GCP may also be considered if you can adapt quickly.

What are agentic AI frameworks and why are they important?

Agentic AI frameworks like LangGraph, CrewAI, and AutoGen allow multiple AI agents to collaborate, making it possible to automate complex workflows rather than just single tasks.

Can someone with strong ML skills but less MLOps experience apply?

Yes, but you’ll need to show a willingness to learn and adapt quickly. Highlighting projects where you’ve deployed or monitored models will strengthen your case.

How does remote work apply to this role in Qatar?

The role can be based in Doha or done remotely, giving flexibility to international candidates as long as they can collaborate effectively across time zones.