Designs, develops, and deploys production-grade AI applications and microservices for data governance, metadata enrichment, profiling, anomaly and duplicate detection, remediation, and automation. Builds machine learning, generative AI, LLM, RAG, and agent-based solutions within sovereign AI environments. Responsibilities include model optimization, API and framework development, security and compliance, CI/CD, testing, documentation, stakeholder collaboration, and production support.
Job Description: AI Software EngineerJob Location: UAERole Summary
The AI Software Engineer will design, develop, and implement AI-enabled solutions that support data management, governance, and automation initiatives across enterprise workstreams. This role is responsible for building scalable AI applications and services that deliver metadata enrichment, data profiling, anomaly detection, duplicate detection, rule recommendations, remediation support, and intelligent automation within an approved sovereign AI environment.
The ideal candidate combines strong software engineering practices with expertise in AI/ML, Generative AI, and data engineering to deliver production-grade AI solutions.
Key ResponsibilitiesAI Solution Development- Design, develop, test, and deploy AI-powered applications and microservices.
- Build scalable AI and machine learning solutions to automate business and data management processes.
- Develop reusable AI frameworks, APIs, and services for enterprise-wide adoption.
- Optimize AI models and applications for performance, reliability, and scalability.
- Develop AI-driven metadata enrichment capabilities to improve data discoverability and governance.
- Build intelligent data profiling solutions to identify patterns, quality issues, and data relationships.
- Implement automated classification and tagging mechanisms for enterprise datasets.
- Design and implement machine learning models for anomaly detection and data quality monitoring.
- Develop duplicate detection and entity-matching algorithms to improve data consistency.
- Continuously evaluate and enhance model accuracy and effectiveness.
- Build AI capabilities that suggest and optimize data quality rules.
- Implement recommendation engines that assist users in resolving data issues.
- Develop intelligent remediation workflows and automated corrective actions.
- Develop LLM-powered solutions and AI agents to streamline business processes.
- Implement prompt engineering, retrieval-augmented generation (RAG), and AI workflow orchestration.
- Integrate Generative AI services into enterprise applications and platforms.
- Ensure AI solutions operate within approved sovereign AI and security environments.
- Follow responsible AI, privacy, governance, and compliance standards.
- Implement security controls, monitoring, and auditability for AI applications.
- Work closely with solution architects, data engineers, product owners, and business stakeholders.
- Translate business requirements into technical AI solutions.
- Participate in code reviews, testing, deployment, and production support activities.
- Maintain technical documentation and promote engineering best practices.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related field.
- 5+ years of software engineering experience with at least 2+ years developing AI/ML solutions.
- Experience delivering enterprise-scale AI applications in production environments.
- Strong understanding of software development lifecycle (SDLC), CI/CD, and DevOps practices.
- Python (mandatory)
- Java, C#, or Node.js
- REST APIs and Microservices Architecture
- Git, DevOps, CI/CD Pipelines
- Scikit-learn, TensorFlow, PyTorch
- Machine Learning Model Development and Deployment
- Anomaly Detection and Pattern Recognition
- NLP and Text Analytics
- Azure OpenAI Service / OpenAI APIs
- Large Language Models (LLMs)
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- AI Agents and Workflow Automation
- SQL and NoSQL Databases
- Data Profiling and Data Quality Frameworks
- Data Governance and Metadata Management Tools
- ETL/ELT and Data Engineering Concepts
- Microsoft Azure (Preferred)
- Azure AI Foundry
- Azure Machine Learning
- Azure Databricks
- Containerization (Docker/Kubernetes)
- Experience working in regulated, government, or sovereign cloud environments.
- Knowledge of data governance, data quality, and master data management.
- Microsoft Azure AI Engineer Associate (AI-102) or equivalent certification.
- Experience implementing Responsible AI and AI Governance frameworks.
- Strong software engineering and system design skills.
- Problem-solving and analytical thinking.
- Ability to translate business needs into AI solutions.
- Effective stakeholder communication.
- Innovation mindset with focus on automation and continuous improvement.
- Strong collaboration and teamwork skills.
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