Senior Digital Solutions Developer

Job reference 1281769

Location Doha, Qatar

Sector Energy - Oil & Gas

Function IT & Telecoms

Employment type Contract

Date published October 13, 2026

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. 

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