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Senior Digital Solutions Developer
- Location: Doha, Qatar
- Working Schedule: Standard Office Hours, 5 Days per Week
Job Purpose & Key Accountabilities
- The Senior Digital Solutions Developer is responsible for designing, developing, and delivering high-quality software systems and AI-powered digital solutions that support the organization's digital transformation strategy.
- Operating within an agile delivery environment, this role drives technical excellence across the full software development lifecycle, from architecture and design through implementation, testing, deployment, and continuous improvement.
- The Senior Digital Solutions Developer serves as a technical anchor within the squad and ensures that digital products are scalable, resilient, secure, and aligned with enterprise architecture standards.
- The role collaborates closely with Product Owners, Project Engineers, Tech Leads, Business Analysts, Data Engineers, and cross-functional stakeholders to translate business requirements into robust technical solutions that generate measurable operational value.
- The Senior Digital Solutions Developer will lead the design and implementation of AI-enabled applications, Retrieval-Augmented Generation (RAG) solutions, document intelligence platforms, semantic search capabilities, AI agents, and applications that process large volumes of structured and unstructured enterprise data.
Job Dimensions
- Team Management
- Provides transversal technical guidance without formal line-management authority.
- Acts with technical authority when working with external technology partners.
- Direct Reports: None.
- Budget Responsibility
- No direct budget ownership.
- Contributes to effort estimation and technical scoping to support the Digital Manager's budget planning and tracking.
- Project Involvement
- Contributes to 2–3 digital products of varying technical complexity.
- Ensures consistent engineering standards and code quality across all deliverables.
- All projects will leverage AI technologies and capabilities.
Activities – (Safety & Working Environment)
- Uphold and be a role model for the company's core values, Incident Free, and leadership behaviours.
- Help maintain motivation, team spirit, and camaraderie.
- Continuously improve ways of working through "inspect and adapt", fostering a growth mindset and collaborative culture across digital stakeholders.
Core Responsibilities
Software Development & Engineering Excellence
- Design, develop, test, deploy, and support scalable, reliable, secure, and maintainable software applications and services.
- Produce clean, well-documented, and maintainable code following established coding standards and best practices.
- Lead technical design sessions and develop software architecture artefacts for new features and system integrations.
- Conduct thorough peer code reviews and provide constructive feedback to improve quality and knowledge sharing.
- Define and enforce unit, integration, and end-to-end testing strategies to ensure software reliability and reduce defect leakage.
- Drive Continuous Integration and Continuous Delivery (CI/CD) practices, including pipeline configuration and automated quality gates.
- Troubleshoot and resolve complex technical issues, performance bottlenecks, and production incidents.
- Evaluate and recommend emerging technologies, frameworks, and engineering practices.
- Contribute to API design, microservices architecture, event-driven systems, and cloud-native deployment patterns on Microsoft Azure.
- Ensure security, compliance, and data governance requirements are embedded throughout the software development lifecycle.
- Manage technical debt through refactoring initiatives and proactive engineering hygiene practices.
- Design, develop, deploy, and support enterprise-grade generative AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search frameworks, and AI-enabled applications.
- Architect and implement document intelligence solutions involving document ingestion, PDF processing, content extraction, metadata enrichment, classification, and enterprise knowledge management workflows.
- Design and optimise unstructured data processing pipelines, including document parsing, chunking strategies, indexing approaches, embedding generation, vector search, and knowledge retrieval frameworks.
- Build and maintain integrations between enterprise applications, vector databases, AI services, and business systems to support intelligent decision-making and information retrieval capabilities.
- Define and implement testing, validation, monitoring, and governance frameworks for AI-powered applications, AI agents, retrieval systems, AI-generated outputs, and non-deterministic AI behaviours.
- Apply AI-assisted engineering methodologies to analyse, modernise, optimise, refactor, and enhance legacy monolithic applications and enterprise software platforms.
Technical Leadership
- Act as the primary technical point of contact for the squad, helping the team resolve engineering blockers and make sound architectural decisions.
- Support the onboarding of new engineers and technology partners, ensuring rapid ramp-up and alignment with engineering standards and best practices.
- Mentor developers and contribute to engineering capability development across the team.
Agile Delivery Collaboration
- Actively participate in agile ceremonies.
- Collaborate with product owners and business analysts to translate user stories and acceptance criteria into detailed technical tasks and effort estimates.
- Support risk identification, dependency management, and sprint planning activities.
- Coordinate with DevOps engineers, data engineers, infrastructure teams, information security teams, and external technology partners to ensure seamless delivery.
- Communicate technical progress, risks, and decisions clearly to technical and non-technical stakeholders.
Minimum Qualifications, Experience & Skills
- Minimum Qualifications
- Bachelor of Science degree in Computer Science, Software Engineering, Information Technology, or a closely related engineering discipline.
- Postgraduate qualification (master's degree or equivalent) in computer science, software engineering, artificial intelligence, data science, or a related field is advantageous.
- Professional Certifications (Preferred)
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure AI Developer Associate
- Microsoft Certified: Azure Solutions Architect Expert
- Microsoft Certified: Azure Developer Associate
- Kubernetes Certification (CKA/CKAD)
- Certified Scrum Developer (CSD)
- Professional Scrum Developer (PSD)
- Minimum Experience
- Minimum of 10 years of hands-on software engineering experience, with at least 4 years in a senior software engineer, technical lead, or software lead capacity.
- Proven track record delivering production-grade software solutions within agile environments.
- Extensive experience building and deploying cloud-native applications on Microsoft Azure.
- Experience designing, developing, deploying, and supporting enterprise AI-powered applications and intelligent automation solutions.
- Proven hands-on production experience delivering Retrieval-Augmented Generation (RAG) solutions within enterprise environments.
- Demonstrated experience processing and managing large-scale unstructured enterprise data, including documents, PDFs, enterprise knowledge repositories, and content management systems.
- Experience implementing document intelligence, semantic search, embedding models, and vector-database-based retrieval solutions.
- Practical experience validating and testing AI-generated outputs, AI workflows, AI agents, and intelligent decision systems.
- Experience applying AI techniques to software modernisation, legacy application analysis, and engineering productivity improvements.
- Experience in industrial sectors, preferably within oil & gas, energy, utilities, or process industries.
Job-Specific Skills (Technical Competencies)
The ideal candidate should possess strong technical expertise across modern software development, cloud technologies, Generative AI, system architecture, DevOps, application security, and enterprise-scale AI solutions.
- Programming Languages: Demonstrated proficiency in one or more modern programming languages including:
- Python (advanced proficiency required, including AI application development, data science, machine learning integration, document intelligence workflows, and Generative AI solution development)
- Java
- C#
- Node.js
- Software Architecture & API Development: Proven experience in designing and implementing scalable, high-performance software architectures including:
- RESTful API design and development
- Microservices architecture
- Event-driven architectures
- Enterprise integration patterns
- Domain-driven design principles
- Front-End Development: Strong experience developing modern, user-centric applications using technologies such as:
- React
- Angular
- Vue.js
Experience developing responsive and cross-platform web applications delivering exceptional user experiences is required.
- Database Technologies: Strong understanding of relational, non-relational, and vector database technologies including:
- Microsoft SQL Server
- PostgreSQL
- MongoDB
- Azure Cosmos DB
- Azure AI Search
- Pinecone
- Weaviate
- ChromaDB
- Elasticsearch Vector Search
Experience in database design, optimisation, performance tuning, semantic indexing, and vector search implementation is highly desirable.
- AI Engineering & Generative AI Solutions: Generative AI & LLM Platforms
- Large Language Models (LLMs)
- AI Agents and Agentic Workflows
- Prompt Engineering
- Enterprise AI Governance
- Responsible AI Practices
- Retrieval-Augmented Generation (RAG)
- Production-grade RAG architectures
- Knowledge retrieval frameworks
- Context management techniques
- Semantic search implementation
- Search relevance optimization
- Unstructured Data Processing & Document Intelligence
- PDF ingestion and processing
- OCR technologies
- Document parsing and information extraction
- Metadata enrichment
- Content classification and tagging
- Enterprise document intelligence platforms
- Embeddings & Vector Technologies
- Embedding generation and optimization
- Vector databases and vector search
- Similarity search algorithms
- Semantic indexing strategies
- Knowledge retrieval frameworks
- AI Testing & Validation
- AI output evaluation methodologies
- AI system validation frameworks
- Hallucination detection and mitigation
- Retrieval quality assessment
- Testing non-deterministic AI systems
- AI observability and monitoring
- Advanced Python & AI Development
- Python for AI and machine learning development
- Data science and analytics workflows
- AI model integration
- Data preprocessing and transformation
- Model orchestration and deployment
- DevOps & CI/CD: Hands-on experience implementing DevOps best practices and managing CI/CD pipelines using:
- Azure DevOps
- GitHub Actions
- Jenkins
Strong knowledge of release automation, deployment strategies, infrastructure-as-code, and software delivery best practices.
- Containerization & Orchestration: Experience deploying and managing containerized applications using:
- Docker
- Kubernetes
- Data Engineering & AI Data Platforms: Practical experience in:
- ETL and ELT development
- Data platform integration
- Structured and unstructured data processing
- Document intelligence workflows
- Data preparation for AI and machine learning solutions
- Enterprise search systems
- Knowledge repositories and content platforms
- Application Security: Strong understanding of secure software development practices including:
- OWASP Top 10 Security Risks
- Secure Coding Standards
- Application Security Testing
- Identity and Access Management
- Security-by-Design principles
Ability to incorporate security considerations throughout the software development lifecycle is essential.
- Critical Requirement: Candidates must demonstrate practical enterprise-level experience implementing and supporting production AI solutions involving:
- Retrieval-Augmented Generation (RAG)
- Unstructured data processing
- Document intelligence solutions
- PDF and content ingestion pipelines
- Chunking and indexing methodologies
- Embedding models
- Vector databases
- Semantic search technologies
- AI testing and validation frameworks
Practical production experience in these areas is considered a critical success factor for the role. Demonstrable delivery experience is required; conceptual understanding alone will not be considered sufficient.