Scavenger Tech

The Full Stack Developer is responsible for designing, developing, and maintaining both frontend user interfaces and backend services in alignment with approved enterprise architecture, security standards, and governance frameworks.

This role ensures that delivered features meet functional and non-functional requirements while providing seamless integration between UI, APIs, databases, and Azure cloud infrastructure.

The position operates within defined architectural boundaries and collaborates closely with Solution Architects, Product Owners, QA, DevSecOps, and UI/UX teams to deliver secure, scalable, high-performance digital solutions.

Role Purpose

Design, develop, and maintain end-to-end application features across frontend and backend layers while ensuring alignment with architectural, security, and performance standards.

Requirements

Must-have

• 4+ years of professional full-stack experience (enterprise web apps)
• Strong TypeScript + React and/or Angular experience (shipping production UIs)
• Strong .NET 6+ experience building ASP.NET Core Web APIs
• Solid SQL Server skills (queries, indexing basics, performance tuning mindset)
• Practical secure coding knowledge (OWASP Top 10; not just buzzwords)
• Experience with authentication/authorization in web apps (JWT/OAuth2)
• Testing culture: unit tests + basic integration/e2e where appropriate
• Git + PR workflows (code reviews, branch protection)
• English B2+ (you can explain security tradeoffs and write clear PRs)

Nice-to-have

• Azure production experience (App Services, Functions, Storage, Key Vault, App Insights)
• Docker basics; microservices patterns; API gateway familiarity
• Experience modernizing legacy codebases / imperfect documentation
• SAST tooling experience (Veracode/Sonar/etc.)
• Arabic fluency

Recommended Certifications

AZ-204 (Azure Developer Associate)

AZ-900 (Azure Fundamentals)

Secure Coding / OWASP Training

Microsoft Security Certifications (Optional but preferred)

Executive Summary

We are seeking a highly experienced technical leader to take ownership of both our forward-looking AI initiatives and the strategic takeover of a mission-critical public safety/mobility platform.

This is a hybrid role bridging executive engineering leadership, AI operationalization, and hands-on solutions architecture. You will be responsible for leading the transition of a complex, mixed-documentation legacy estate (ASP.NET 4.8/WCF) from an external vendor, designing its modernization path to .NET 8, and simultaneously building out production-ready, localized AI/ML capabilities.

The ideal candidate is a pragmatic, highly technical leader who can walk into a complex legacy environment, make it understandable, steadily modernize it without breaking production, and seamlessly integrate advanced AI/ML capabilities within strict governance frameworks.

The Real-World Context

• You will see legacy code and uneven documentation. Some services are modern .NET 8; others are ASP.NET Framework 4.8 and WCF relying on "tribal knowledge."
• You will own the vendor transition. You need to define the boundaries, repo strategy, and handover steps to move ownership safely in-house.
• You will build the future. You will have the authority and influence to leave this platform cleaner, safer, and AI-enabled, transitioning from partly manual deployments to automated, secure DevSecOps pipelines.

Key Responsibilities

1. Platform Takeover & Architecture Modernization

• Map the As-Is: Figure out what is currently running. Map components, integrations, environments, and deployment paths of the vendor-operated platform.
• Own the Modernization Path: Design a phased, risk-controlled architectural roadmap from ASP.NET 4.8/WCF to a clean .NET 8 (LTS) microservices ecosystem.
• Set the Standards: Establish API contracts, coding standards, and Architecture Decision Records (ADRs) to ensure the new internal team operates consistently.

2. Engineering Delivery & DevSecOps

• Execute the software development lifecycle (SDLC) in line with approved governance and Zero Trust security principles.
• Partner with Ops/DevOps to replace manual deployment habits with auditable CI/CD pipelines (approval gates, mandatory SAST scanning, artifact integrity, zero-downtime rollbacks).
• Oversee technical delivery timelines, resource allocation, and overall release readiness.

3. AI Implementation & Operationalization

• Execute the AI roadmap, leading the development, integration, and deployment of AI/ML models (NLP, Computer Vision, Predictive Analytics).
• Implement MLOps pipelines and full model lifecycle management to ensure AI solutions are scalable, monitored, and production-ready.
• Ensure AI systems strictly comply with data governance and enterprise security policies.

4. Cloud Infrastructure & System Integration

• Implement cloud architecture standards within Azure, optimizing for both modern AI workloads (GPU, model hosting) and legacy .NET integrations.
• Oversee complex data architectures bridging legacy and modern systems (SQL Server, MySQL).
• Monitor cost optimization, system performance, and infrastructure scalability.

5. Team Leadership & Localized AI Optimization

• Build, manage, and mentor an onsite, high-signal team of senior engineers, architects, and AI specialists.
• Translate tribal knowledge into structured, understandable documentation for the team.
• Supervise AI localization, ensuring Arabic NLP tuning, validation, and regional adaptability are culturally and linguistically precise.

6. Stakeholder Communication & Authority

• Translate deep technical challenges (both legacy untangling and AI scaling) into structured risk reports for executive leadership.
• Present updates clearly in both Arabic and English.
• Authority Level: Approves technical implementations, enforces governance, and escalates architectural risks. (Cannot override enterprise architecture board decisions).

Required Qualifications

• Education: Master’s or PhD in Computer Science, AI, Data Science, or a related technical field (or equivalent extensive architectural experience).
• Experience: 10+ years of engineering experience, with 3-4+ years in a dual architecture/AI leadership role.
• Modernization Reality: Proven experience in "vendor takeover" scenarios, legacy decoupling, and migrating legacy .NET to modern frameworks.
• Tech Stack Fluency: Deep, hands-on knowledge across ASP.NET Framework 4.8 + WCF, .NET 8 (LTS), SQL Server, and MySQL.
• AI Leadership: Proven experience deploying and operationalizing AI systems (MLOps) in production environments.
• Language: Native or professional fluency in Arabic and English (written and verbal).
• Location: Must be able to work 100% onsite at the Dubai office.

Core & Technical Competencies

• Hybrid Architecture: Ability to bridge legacy enterprise patterns with modern distributed/microservices architectures.
• Azure Cloud Execution: Deep implementation knowledge of Azure-native services, cloud scalability, and migration paths.
• AI/MLOps Mastery: Moving models from POC to monitored, production-grade environments (drift detection, retraining strategies).
• Governance & Risk: Identifying architectural and scalability risks, and structured decision-making within strict security/compliance mandates.
• Frontend/Integration (Nice to have): Working knowledge of React/Angular ecosystems to design BFF (Backend for Frontend) approaches, Python, or mobile integrations.

Required Technical Certifications

• AZ-204 – Azure Developer Associate
• AZ-104 – Azure Administrator Associate
• AI-102 – Azure AI Engineer Associate
• AZ-305 – Azure Solutions Architect Expert
• AZ-400 – Azure DevOps Engineer Expert
• DP-100 – Azure Data Scientist Associate

Optional but Valuable: Kubernetes on Azure (AKS), Azure Security Engineer (AZ-500), Advanced MLOps/Model Monitoring specializations.

To Apply: Please include a short note detailing the largest legacy system you have modernized (what it was, what you changed, and how you managed risk) alongside your experience in operationalizing AI systems.

About the role
You’ll design and maintain automation frameworks and integrate quality validation into CI/CD pipelines, while owning performance engineering and NFR compliance (scalability, reliability, response time). You play a key role in release governance by identifying performance bottlenecks and automation gaps before production.

What you’ll do

• Build reusable automation frameworks; UI + API automated scripts; maintain regression automation suite; reduce flaky tests; publish coverage/execution reports 
• Integrate automated tests into Azure DevOps pipelines; enforce quality gates; PR/merge triggers; block faulty builds; collaborate with DevSecOps on validation standards 
• Design and execute load/stress/spike/endurance tests; validate scalability under concurrency; measure latency/throughput/resource usage 
• Identify bottlenecks (app/db/api/infra); analyze SQL query plans; evaluate API latency; propose tuning recommendations with dev/architects 
• Validate NFR compliance and performance thresholds; participate in Go/No-Go; escalate risks when thresholds fail 
• Manage test data and repeatable environments; support Docker/containerized testing environments 

Must-have

• 5+ years automation and/or performance testing; CI/CD experience; Azure enterprise environment exposure; performance diagnostics/monitoring 
• Automation: Selenium/Playwright/Cypress; API automation; scripting (C# or JavaScript); POM/modular framework design 
• CI/CD: Azure DevOps pipelines, build validation triggers, quality gate configuration, pipeline debugging/optimization 
• Performance: JMeter/k6, latency/throughput analysis, capacity modeling; monitoring (Azure Monitor/App Insights/log analysis) 
• SQL performance analysis + execution plan review 

Authority / constraints

• Owns automation framework standards and coverage expectations; can escalate performance risks; must collaborate for structural redesign; cannot change scope or bypass functional QA 

Nice-to-have certs

• ISTQB Test Automation Engineer; AZ-204 / AZ-400 / AZ-104 / AZ-305 recommended options; advanced performance testing training

About the role
You’ll validate functional quality and ensure requirements are met before staging/production release, enforcing structured test processes, traceability (RTM), and disciplined validation cycles. You have authority to block releases when acceptance criteria are not met.

What you’ll do

• Review requirements/user stories/acceptance criteria; design test cases (positive/negative/boundary/edge); maintain ADO Test Plans + RTM 
• Execute functional/regression/integration cycles; validate acceptance criteria; exploratory + smoke testing before release 
• Log reproducible defects with evidence; track lifecycle; verify fixes; analyze root-cause trends 
• Maintain/expand regression suite based on defect trends; revalidate impacted modules during updates 
• API testing (Postman) + validate request/response/error handling; basic SQL queries for backend data validation 
• Validate basic OWASP scenarios and access-control testing; support pre-prod validation checks 
• Participate in Go/No-Go; provide formal QA sign-off; block release for critical/high-severity defects 

Must-have

• 5+ years QA/testing; enterprise applications; Agile/Scrum; structured test management tools 
• Test case design/execution, regression management, acceptance criteria validation, RTM discipline 
• API testing (Postman) + basic SQL (SELECT/JOIN/filters) + data validation techniques 
• ADO/TFS/Jira defect lifecycle; severity/priority classification; trend analysis 
• Awareness: SDLC stages, ADO Test Plans, basic OWASP Top 10, basic CI/CD understanding 

Authority / constraints

• Can block releases; can reject unclear requirements for testing; cannot change scope/logic or override architecture/security standards 

Nice-to-have certs

• ISTQB Foundation; AZ-900 recommended; OWASP awareness training

About the role
You’ll build enterprise dashboards, reporting systems, and automation workflows, ensuring KPI standardization, reporting accuracy, and data governance compliance while improving operational efficiency through structured automation.

What you’ll do

• Build Power BI dashboards aligned to business KPIs; advanced DAX measures; standardized templates; performance optimization 
• Design data models and ETL pipelines; validate data quality; maintain dimensional modeling standards; optimize SQL performance 
• Own KPI governance: formal KPI definitions/formulas, centralized KPI dictionary, prevent conflicting metrics 
• Build Power Automate workflows (approvals/notifications/triggers) with reliability + exception handling; API-based automation integration 
• Enforce data governance/security: RBAC + Power BI row-level security; compliance with access policies 
• Validate outputs vs source systems; reconciliation checks; audit trails; RCA for discrepancies 

Must-have

• 5+ years BI/data analytics; enterprise reporting; strong relational DB background; Agile experience 
• Power BI (advanced DAX), data modeling (star/snowflake), report optimization 
• SQL Server (advanced queries, stored procedures, indexing) 
• ETL: SSIS or Azure Data Factory preferred; data warehouse concepts 
• Power Automate (cloud/desktop flows), REST/JSON integrations, orchestration logic 
• Azure data services (Azure SQL, ADF, Synapse) + governance (RBAC/RLS, privacy/compliance) 

Authority / constraints

• Owns KPI modeling and automation framework principles; cannot redefine KPIs without PO/business alignment; cannot bypass governance/access policy 

Certs

• PL-300 mandatory; DP-203 recommended

About the role
You’ll validate AI-driven systems (ML models, LLM solutions, and Agentic AI workflows) to ensure outputs are accurate, reliable, secure, and compliant with governance standards. Focus areas include model behavior, prompt reliability, automation flows, and enterprise integration testing.

What you’ll do

• Test ML models using metrics (accuracy, precision, recall, F1) and adversarial/edge-case testing 
• Validate LLM outputs for correctness, relevance, and hallucination control 
• Run prompt-variant testing for response consistency; detect bias/incorrect reasoning; enforce business rules 
• Test multi-agent workflows, orchestration, decision logic, fallback handling, automation scenarios 
• Perform end-to-end scenario testing across APIs/services; validate real-time inference pipelines 
• Governance/compliance testing: RBAC/data access controls, sensitive data handling, auditability/traceability 
• Performance/reliability: load/latency/scalability testing; monitor drift/degradation over time 

Must-have

• 5+ years QA/testing with exposure to AI systems; experience testing ML models, APIs, enterprise apps 
• LLM evaluation techniques (hallucination detection, prompt testing) + dataset validation/test data generation 
• Automation tools: Selenium/Cypress/Playwright; API testing (Postman/REST Assured) 
• Automation scripting (Python or JavaScript); basic Python for AI pipeline testing 
• Azure environment awareness; CI/CD pipelines (Azure DevOps); logging/monitoring awareness 
• Familiarity with embeddings/vector DBs and ML/LLM workflow concepts 

Authority / constraints

• Owns AI testing standards/metrics and acceptance criteria; cannot approve release without quality thresholds 

Nice-to-have certs

• ISTQB Advanced Test Analyst preferred; AI-900 recommended

About the role
We’re hiring a Senior AI Engineer to design, develop, and deploy enterprise AI solutions including Agentic AI, intelligent automation, and predictive analytics, aligned with governance/security frameworks and business objectives.

What you’ll do

• Build AI/ML models (classification, prediction, NLP, computer vision) and optimize for performance/cost 
• Build LLM-based solutions (chatbots, copilots, automation agents) 
• Design and implement multi-agent architectures, orchestration logic, decision flows, and human-in-the-loop controls 
• Own data prep, feature engineering pipelines, training/fine-tuning/evaluation, and model lifecycle/versioning 
• Integrate AI into enterprise systems via APIs/microservices, including real-time inference services 
• Implement responsible AI practices (bias control, explainability, auditability) and secure AI pipelines/APIs (auth/authz) 
• Monitor production performance, logging/alerting, drift, and optimize latency/scalability 

Must-have

• 5+ years AI/ML engineering; production deployments; enterprise-scale AI systems/APIs 
• Hands-on LLMs/NLP or Computer Vision 
• Strong Python ML stack (TensorFlow/PyTorch/Scikit-learn) 
• LLM ecosystem experience (Azure OpenAI / OpenAI / Gemini / HuggingFace), embeddings/vector DBs 
• Agentic/orchestration frameworks (LangChain / Semantic Kernel / AutoGen; multi-agent frameworks; tool use) 
• Azure AI Services/Azure ML/Databricks + Docker/Kubernetes; REST/microservices, event-driven, APIM 

Authority / constraints

• Owns AI architecture/model development standards and Agentic AI implementation approaches 
• Cannot deploy without security/governance approval; cannot use external AI services without compliance validation 

Nice-to-have certs

• AI-102 preferred; DP-100 recommended