Senior Specialist, Data Science & Artificial Intelligence

Why This Role Matters What You Will Deliver AI Solution Development & Innovation

• Develop and deploy machine learning, deep learning, and Generative AI solutions that address strategic and operational business challenges.
• Design scalable AI applications that improve decision-making, productivity, automation, and business performance.
• Accelerate AI adoption through innovative use of advanced analytics and emerging AI technologies.

Model Performance & Optimization

• Improve model accuracy, reliability, and effectiveness through feature engineering, tuning, evaluation, and optimization techniques.
• Strengthen AI outcomes through robust testing, validation, and continuous performance enhancement.
• Ensure AI solutions remain aligned with business objectives and evolving operational requirements.

Generative AI & LLM Enablement

• Build and enhance GenAI applications using prompt engineering, retrieval-augmented generation (RAG), and large language model technologies.
• Improve response quality, accuracy, and relevance through structured evaluation and optimization approaches.
• Deliver enterprise-ready AI capabilities that support knowledge discovery, content generation, and intelligent automation.

Data Preparation & Engineering Enablement

• Transform structured and unstructured data into high-quality datasets suitable for AI and machine learning applications.
• Improve data usability and reliability through effective cleansing, preparation, and feature development practices.
• Ensure AI solutions are built on trusted, governed, and business-relevant data assets.

AI Operations & Lifecycle Management

• Support deployment, monitoring, retraining, and lifecycle management of AI solutions using MLOps and LLMOps practices.
• Improve operational reliability and scalability of production AI models and applications.
• Enable sustainable AI adoption through effective performance monitoring and continuous improvement.

Governance, Risk & Responsible AI

• Ensure AI solutions comply with enterprise governance, cybersecurity, privacy, and ethical AI requirements.
• Strengthen transparency and trust by documenting models, assumptions, risks, and validation outcomes.
• Promote responsible AI practices that balance innovation with risk management and compliance obligations.

What Success Looks Like

• AI and machine learning solutions deliver measurable business value and operational improvement.
• Generative AI applications provide accurate, reliable, and high-quality outputs for end users.
• Machine learning models achieve performance targets and remain effective throughout their lifecycle.
• AI solutions are successfully integrated into enterprise processes and systems.
• MLOps and LLMOps practices improve model reliability, scalability, and operational efficiency.
• Governance, security, and responsible AI requirements are consistently embedded within AI initiatives.

Minimum Qualifications

• Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative discipline.

Experience

• 4–6 years of experience in data science, machine learning, artificial intelligence, or advanced analytics roles.
• Experience developing and deploying machine learning and AI solutions in business environments.
• Experience working with structured and unstructured data for analytical and AI use cases.
• Experience supporting AI solution deployment, monitoring, and model lifecycle management.

Skills That Matter Functional Expertise

• Machine Learning & Deep Learning
• Generative AI & Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Model Fine-Tuning & Evaluation
• Data Preparation & Feature Engineering

Business & Delivery

• Python & SQL
• TensorFlow, PyTorch & Scikit-Learn
• MLOps & LLMOps Practices
• API Integration & AI Deployment
• Model Monitoring & Performance Optimization
• Analytical Problem Solving

People & Collaboration

• Collaboration & Teamwork
• Stakeholder Engagement
• Communication & Knowledge Sharing
• Results Orientation
• Accountability
• Continuous Improvement