Senior Artificial Intelligence Specialist

Job Title: Senior Artificial Intelligence (AI) Specialist
Location: Bank Muscat Head Office
Department: Enterprise Project Management & Innovation
Job Overview:
We are seeking an innovative and detail-oriented Senior AI Specialist to design and implement AI-driven solutions to solve business challenges. As a core member of the team, you will be responsible for building, deploying, and maintaining AI models that drive automation, optimize business processes, and enable predictive decision-making across our organization. The specialist will work with various datasets, designing efficient AI systems / use cases, integrating them with existing IT infrastructure, and ensuring that AI tools are scalable and reliable. The role also includes a focus on machine learning (ML), deep learning (DL), and natural language processing (NLP).
The successful candidate will act as an internal consultant on all things AI, championing the use of advanced analytics and data science across departments, and working closely with data engineers, IT architects, and business stakeholders to turn theoretical models into practical applications.

Key Responsibilities:
AI Solution Development:
Design and Architecture: Lead the design and development of AI systems that address key business challenges, ensuring these systems are robust, scalable, and integrate seamlessly into the company’s technology landscape.
Algorithm Selection: Research and select appropriate AI models, machine learning algorithms, and deep learning frameworks for specific tasks (e.g., regression, classification, clustering, recommendation systems, etc.).
Model Training and Evaluation: Train AI models using supervised, unsupervised, and reinforcement learning techniques; evaluate model performance using metrics like accuracy, precision, recall, and ensure they meet performance standards.
NLP Implementation: Develop and optimize natural language processing algorithms to enhance language understanding in various applications, such as chatbots, sentiment analysis, and automated customer service.

Data Strategy and Management:
Data Preparation: Collaborate with data engineering teams to design and implement ETL (Extract, Transform, Load) pipelines, ensuring data is clean, organized, and usable for AI applications.
Data Governance: Ensure the ethical use of AI through appropriate data governance practices, including adherence to data privacy standards, such as GDPR and CBO’s Financial Consumer Protection Regulatory Framework, and the implementation of responsible AI principles.
Data Augmentation: Enhance data through augmentation techniques, using synthetic data where necessary to bolster training datasets.

System Integration and Deployment:
Integration: Work with software developers and IT teams to deploy AI solutions and integrate them with existing systems, such as ERP, CRM, and cloud platforms.
Continuous Improvement: Develop automated systems to monitor AI performance post-deployment and ensure continuous learning, allowing the system to adapt and improve over time.
Version Control and Reproducibility: Utilize version control tools to ensure all AI models and code are reproducible and traceable.

Automation and Process Optimization:
AI-Driven Automation: Develop AI-based automation solutions, such as intelligent workflows, to streamline business processes e.g., supply chain management, customer service, and IT operations.
Predictive Analytics: Implement predictive models to forecast business metrics like customer churn, sales trends, and operational efficiency.

Collaboration and Consultation:
Cross-Functional Collaboration: Work closely with cross-departmental teams (Marketing, Operations, Finance, etc.) to identify AI opportunities and translate business needs into technical requirements.
Training and Knowledge Sharing: Educate stakeholders and internal teams on AI trends, tools, and best practices, ensuring widespread adoption and understanding of AI across the organization.

Research and Development:
Innovation: Stay abreast of the latest advancements in AI research, including emerging frameworks, algorithms, and hardware optimizations (e.g., GPUs, TPUs, quantum computing).
Experimentation: Lead experiments on cutting-edge AI applications, including generative AI, reinforcement learning, and neural network innovations.
R&D Roadmaps: Contribute to AI R&D roadmaps, proposing initiatives that can push the boundaries of what the company can achieve with AI in areas like personalization, AI ethics, and human-machine collaboration.

Qualifications:
Educational Requirements: Minimum: Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Preferred: Master’s degree in a relevant discipline, with specialization in AI, machine learning, or data science.
Experience Requirements: AI and ML Expertise: 7+ years of hands-on experience in AI and machine learning, with a proven track record of building and deploying large-scale AI systems.
Industry Exposure: Experience in AI implementation within a financial services, retail, healthcare, or technology company is highly desirable.
Project Leadership: Demonstrated experience leading AI-focused projects from conception to production, including experience managing teams or mentoring junior staff.

Technical Skills:
Programming Languages: Proficiency in Python, R, Java, or C++, with strong hands-on experience in AI/ML libraries such as TensorFlow, PyTorch, Keras, and scikit-learn.
Data Handling: Strong knowledge of SQL and NoSQL databases, data lake architectures, and big data processing technologies
Cloud Proficiency: Experience with AI services on cloud platforms like AWS (SageMaker), Microsoft Azure (Cognitive Services), GCP (Vertex AI), Oracle cloud database.
Modeling Techniques: Expertise in deep learning, neural networks (CNNs, RNNs, LSTMs, GANs), NLP, computer vision, and reinforcement learning.
Software Engineering: Knowledge of software development methodologies, including Agile, and version control systems
Power BI & Power Query; Data Visualization, DAX
Both Front End & Back end of data migration integration cleaning
Preferable knowledge in Oracle
Certifications (Preferred but Not Required):
AWS Certified Machine Learning – Specialty
Microsoft Certified: Azure AI Engineer Associate

Key Competencies:
Analytical Problem Solving: Strong analytical skills with the ability to synthesize complex data and provide actionable insights.
Innovation and Creativity: Passion for leveraging AI in novel ways to solve business problems and improve operational efficiency.
Leadership and Initiative: Ability to lead AI initiatives independently and take ownership of the AI lifecycle within the organization.
Attention to Detail: Meticulous with model validation, ensuring AI solutions deliver high-quality, accurate results.
Collaboration and Communication: Exceptional communication skills, able to bridge the gap between technical teams and non-technical stakeholders.
Managing, and understanding Key Stake holders’ requirements.