Urwah Admin

Contango

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 About Contango
 
 
 
 Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
 
 
 
 Role Overview
 
 
 
 As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end-to-end Data &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact.
 
 
 
 Key Responsibilities
 
 
 
 
 Use Case Framing &amp; Solution Design
 
 
 
 <ul>
 <li>
 Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.
 </li>
 <li>
 Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.
 </li>
 <li>
 Make explicit trade-offs across performance, cost, latency, and maintainability.
 </li>
 <li>
 Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).
 
 
 </li>
 </ul>
 
 Data Engineering &amp; Feature Systems
 
 
 
 <ul>
 <li>
 Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.
 </li>
 <li>
 Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.
 </li>
 <li>
 Collaborate with platform teams to define data models, schemas, and storage strategies.
 </li>
 <li>
 Enforce standards for data validation, testing, and monitoring within production systems.
 
 
 </li>
 </ul>
 
 Applied ML &amp; Production-Grade Development
 
 
 
 <ul>
 <li>
 Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.
 </li>
 <li>
 Structure codebases into maintainable, testable modules, with clear separation of concerns.
 </li>
 <li>
 Implement unit, integration, and end-to-end tests for data and ML components.
 </li>
 <li>
 Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.
 </li>
 <li>
 Balance model sophistication with system performance, latency, and operational constraints.
 
 
 </li>
 </ul>
 
 MLOps, DevOps &amp; Platform Integration
 
 
 
 <ul>
 <li>
 Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.
 </li>
 <li>
 Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.
 </li>
 <li>
 Implement model versioning, experiment tracking, and artifact management.
 </li>
 <li>
 Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.
 </li>
 <li>
 Automate retraining, rollback, and release strategies to ensure system resilience.
 
 
 </li>
 </ul>
 
 System Reliability, Scalability &amp; Security
 
 
 
 <ul>
 <li>
 Design systems for high availability, fault tolerance, and horizontal scalability.
 </li>
 <li>
 Optimize performance across data pipelines and inference services (latency, throughput, cost).
 </li>
 <li>
 Apply secure coding practices, access controls, and data protection standards.
 </li>
 <li>
 Manage technical debt and ensure long-term maintainability of production systems.
 
 
 </li>
 </ul>
 
 Documentation, Standards &amp; Engineering Excellence
 
 
 
 <ul>
 <li>
 Produce developer-focused documentation (APIs, architecture diagrams, runbooks).
 </li>
 <li>
 Establish and enforce coding standards, review processes, and engineering best practices.
 </li>
 <li>
 Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.
 </li>
 <li>
 Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.
 
 
 </li>
 </ul>
 
 Required Experience And Qualifications
 
 
 
 <ul>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Client-serving experience is an advantage.
 </li>
 <li>
 5+ years of experience in data science or a related analytical field.
 </li>
 <li>
 5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.
 </li>
 <li>
 Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.
 </li>
 <li>
 Prior experience in developing AI solutions on public cloud services is an advantage.
 </li>
 <li>
 Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).
 </li>
 <li>
 Technical Expertise:
 </li>
 <ul>
 <li>
 Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.
 </li>
 <li>
 Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.
 </li>
 <li>
 ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).
 </li>
 <li>
 Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.
 </li>
 </ul>
 </ul>
</div>

Senior Data Scientist (6 months contract)

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: July 2026 
Location: Abu Dhabi (on-site) 
About Contango:
Contango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.
Role Overview
As a Senior Data Scientist, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end-to-end Data &amp; AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact. 
Key Responsibilities
Use Case Framing &amp; Solution Design<ul><li>
Translate client business problems into end-to-end system architectures that combine Data, ML, and software components.</li><li>
Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.</li><li>
Make explicit trade-offs across performance, cost, latency, and maintainability.</li><li>
Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).</li></ul>
Data Engineering &amp; Feature Systems<ul><li>
Design and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.</li><li>
Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.</li><li>
Collaborate with platform teams to define data models, schemas, and storage strategies.</li><li>
Enforce standards for data validation, testing, and monitoring within production systems.</li></ul>
Applied ML &amp; Production-Grade Development<ul><li>
Develop ML solutions using production-quality code (Python/JS), following software engineering best practices.</li><li>
Structure codebases into maintainable, testable modules, with clear separation of concerns.</li><li>
Implement unit, integration, and end-to-end tests for data and ML components.</li><li>
Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.</li><li>
Balance model sophistication with system performance, latency, and operational constraints.</li></ul>
MLOps, DevOps &amp; Platform Integration<ul><li>
Build and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.</li><li>
Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.</li><li>
Implement model versioning, experiment tracking, and artifact management.</li><li>
Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.</li><li>
Automate retraining, rollback, and release strategies to ensure system resilience.</li></ul>
System Reliability, Scalability &amp; Security<ul><li>
Design systems for high availability, fault tolerance, and horizontal scalability.</li><li>
Optimize performance across data pipelines and inference services (latency, throughput, cost).</li><li>
Apply secure coding practices, access controls, and data protection standards.</li><li>
Manage technical debt and ensure long-term maintainability of production systems.</li></ul>
Documentation, Standards &amp; Engineering Excellence<ul><li>
Produce developer-focused documentation (APIs, architecture diagrams, runbooks).</li><li>
Establish and enforce coding standards, review processes, and engineering best practices.</li><li>
Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.</li><li>
Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.</li></ul>
Required Experience and Qualifications<ul><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Client-serving experience is an advantage.</li><li>
5+ years of experience in data science or a related analytical field.</li><li>
5+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.</li><li>
Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.</li><li>
Prior experience in developing AI solutions on public cloud services is an advantage.</li><li>
Bachelor&rsquo;s degree (Master&rsquo;s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).</li><li>
Technical Expertise:<ul><li>
Coding &amp; data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.</li><li>
Statistics &amp; experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.</li><li>
ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).</li><li>
Data prep &amp; analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.</li></ul></li></ul></div>

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced DevOps Engineer to make software delivery fast, repeatable, and safe. You&rsquo;ll build and operate CI/CD pipelines, infrastructure-as-code, container platforms, and observability &mdash; cutting lead time and toil while improving reliability and security.
 
 
 The discipline has shifted toward platform engineering: building internal developer platforms and golden paths so product teams can self-serve, with GitOps, policy-as-code, and supply-chain security built in. DevSecOps and FinOps are core parts of the job here, not afterthoughts.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and maintain CI/CD pipelines with automated testing, security scanning, and progressive delivery.
 </li>
 <li>
 Author infrastructure as code with Terraform/OpenTofu using reusable modules and clean environment promotion.
 </li>
 <li>
 Build and operate Kubernetes platforms &mdash; Helm/Kustomize packaging, autoscaling, and ingress.
 </li>
 <li>
 Implement GitOps with Argo CD or Flux so the desired state of infrastructure and apps lives in Git.
 </li>
 <li>
 Stand up observability: metrics, logs, traces, dashboards, SLOs, and alerting.
 </li>
 <li>
 Embed DevSecOps: SAST/DAST/SCA scanning, secrets management, image hardening, and software supply-chain security.
 </li>
 <li>
 Build internal developer platforms and golden paths that enable self-service for product teams.
 </li>
 <li>
 Manage cloud infrastructure with cost awareness, automate operational tasks, and improve DORA metrics; participate in on-call and run blameless post-mortems.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.
 </li>
 <li>
 3+ years in DevOps, SRE, platform, or infrastructure engineering.
 </li>
 <li>
 Strong Linux fundamentals and scripting in Bash and Python (Go a plus).
 </li>
 <li>
 Production experience with a major cloud (AWS, Azure, or GCP).
 </li>
 <li>
 Hands-on experience running Docker and Kubernetes in production.
 </li>
 <li>
 Proficiency with infrastructure as code (Terraform/OpenTofu) and a CI/CD system (GitHub Actions, GitLab CI, or Jenkins).
 </li>
 <li>
 Solid grasp of networking, DNS, TLS, load balancing, and security fundamentals.
 </li>
 <li>
 Experience with monitoring/observability tooling and incident management.
 
 
 </li>
 </ul>
 
 Nice To Have
 
 
 
 <ul>
 <li>
 Operating ML/AI infrastructure (GPU clusters, model serving, MLOps/LLMOps pipelines).
 </li>
 <li>
 Multi-cloud or hybrid-cloud architecture and migrations.
 </li>
 <li>
 Chaos engineering and advanced reliability practices.
 </li>
 <li>
 eBPF-based tooling (Cilium, Pixie) and custom Kubernetes operators.
 </li>
 <li>
 Certifications such as CKA/CKAD, a cloud architect cert, or Terraform Associate.
 
 
 </li>
 </ul>
 
 Tools &amp; Technologies You&rsquo;ll Work With
 
 
 
 <ul>
 <li>
 Scripting: Bash, Python, Go; YAML, HCL.
 </li>
 <li>
 CI/CD: GitHub Actions, GitLab CI, Jenkins, Azure DevOps.
 </li>
 <li>
 IaC &amp; config: Terraform/OpenTofu, Pulumi, Ansible, Crossplane.
 </li>
 <li>
 Containers &amp; GitOps: Docker, Kubernetes, Helm, Kustomize, Argo CD, Flux, Argo Rollouts.
 </li>
 <li>
 Observability: Prometheus, Grafana, OpenTelemetry, Loki, ELK/OpenSearch, Datadog.
 </li>
 <li>
 DevSecOps: Trivy, Snyk, SonarQube, Checkov, OPA/Kyverno; Sigstore, SLSA, SBOMs.
 </li>
 <li>
 Secrets &amp; platform: HashiCorp Vault, External Secrets, OIDC workload identity; Backstage; AWS, Azure, GCP.
 </li>
 </ul>
</div>

DevOps Engineer (6 months contract)

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced Data Engineer to design, build, and operate scalable, secure, and reliable data pipelines and platforms that power analytics and AI solutions for companies in the UAE. The role is hands-on and client-facing, owning data solutions end-to-end &mdash; from architecture and development to production and hypercare &ndash; on cloud and on-premise.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design, build, and maintain scalable batch and streaming data pipelines (ETL/ELT) that move data reliably from source systems to analytics and AI workloads.
 </li>
 <li>
 Build and optimize data models, databases, data warehouses, and lakehouse architectures (e.g., Databricks, Microsoft SQL Server) for performance and cost.
 </li>
 <li>
 Develop and tune distributed processing jobs using Spark/PySpark, including cluster management and optimization.
 </li>
 <li>
 Ensure data quality and data governance across the full data lifecycle (collection, access, storage, transformation, retention).
 </li>
 <li>
 Productionize pipelines with CI/CD, orchestration (e.g., Airflow, Azure Data Factory), monitoring, and alerting.
 </li>
 <li>
 Build the data foundation for analytics and AI use cases, collaborating closely with Data Scientists and AI Engineers (e.g., feature engineering, RAG data processing).
 </li>
 <li>
 Translate client business requirements into data architecture decisions and communicate trade-offs to technical and non-technical stakeholders.
 </li>
 <li>
 Maintain technical documentation, data lineage, and delivery artifacts across Design, Build, UAT, Production &amp; Hypercare.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Strong programming in Python and SQL with solid software engineering fundamentals.
 </li>
 <li>
 Hands-on experience with Spark/PySpark and big-data processing at scale.
 </li>
 <li>
 Proven experience building production ETL/ELT pipelines.
 </li>
 <li>
 Practical knowledge of a major cloud data stack &mdash; Azure, AWS, or GCP.
 </li>
 <li>
 Experience with a modern data platform (Databricks, Snowflake, or equivalent).
 </li>
 <li>
 Familiarity with orchestration, CI/CD, and DevOps (e.g., Airflow, Git, Docker).
 </li>
 <li>
 Understanding of data warehousing concepts, data modeling, data governance, and security best practices.
 </li>
 <li>
 Experience with dashboard or frontend engineering is a plus.
 </li>
 <li>
 Strong collaboration and communication skills, with the ability to work directly with clients.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.
 </li>
 <li>
 3&ndash;6 years of experience in data engineering, building and operating data pipelines at scale.
 </li>
 <li>
 Preferred certifications: cloud data certifications such as Azure Data Engineer, AWS Data Engineer, or Databricks Data Engineer.
 </li>
 </ul>
</div>

Data Engineer (6 months contract)

<div>
 
 Contract duration:
 
 6 months (with a potential extension)
 
 
 
 Engagement Type:
 
 Full-time
 
 
 
 Start date:
 
 July 2026
 
 
 
 Location:
 
 Abu Dhabi (on-site)
 
 
 
 Role Overview
 
 
 
 We are seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems &mdash; primarily LLM-powered applications, RAG pipelines, and agentic workflows &mdash; that solve real client business problems in the UAE. The role combines applied AI expertise with solid software and data engineering foundations, with hands-on ownership from prototype to production and hypercare.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Design and deliver end-to-end LLM applications: scope the use case, build, evaluate, deploy, and hand over reliable, observable services.
 </li>
 <li>
 Build and optimize RAG pipelines: data parsing, ingestion, chunking, embeddings, vector search, re-ranking, and context-window optimization.
 </li>
 <li>
 Design and orchestrate agentic workflows for reasoning, planning, and task execution.
 </li>
 <li>
 Build evaluation and observability: define metrics (accuracy, latency, cost-per-task), run eval pipelines, and monitor systems in production.
 </li>
 <li>
 Integrate AI components into client applications via secure backend APIs; collaborate with data, software, and DevSecOps teams.
 </li>
 <li>
 Engineer the supporting data layer: pipelines, feature/embedding stores, and data quality for AI workloads on cloud platforms.
 </li>
 <li>
 Manage the full lifecycle: deployment, monitoring, versioning, rollback readiness, and cost/performance optimization.
 </li>
 <li>
 Translate business problems into AI solutions and communicate model capabilities, limitations, and trade-offs clearly to clients and stakeholders.
 </li>
 <li>
 Maintain technical documentation, runbooks, and delivery artifacts across Design &amp; Align &rarr; Build &rarr; UAT &rarr; Production &amp; Hypercare.
 
 
 </li>
 </ul>
 
 Candidate Requirements
 
 
 
 <ul>
 <li>
 Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.
 </li>
 <li>
 Prior experience at management consulting firms and/or Big Tech is an advantage.
 </li>
 <li>
 Strong Python and solid software engineering fundamentals.
 </li>
 <li>
 Hands-on LLM APIs (Anthropic, OpenAI, Azure OpenAI), including tokenization, latency, and cost control.
 </li>
 <li>
 Proven RAG architecture experience with vector databases (e.g., Azure AI Search, Qdrant, Chroma, Pinecone, FAISS).
 </li>
 <li>
 Experience with agent/orchestration frameworks (e.g., LangChain, LangGraph, CrewAI).
 </li>
 <li>
 Evaluation and observability skills for production AI (eval pipelines, LLM-as-judge, monitoring).
 </li>
 <li>
 Working knowledge of a major cloud AI/data stack (Azure, AWS, or GCP) and MLOps tooling (e.g., MLflow, Docker).
 </li>
 <li>
 Solid data engineering skills: SQL, distributed processing (e.g., Spark/Databricks).
 </li>
 <li>
 Strong collaboration and communication skills &mdash; able to work with both technical teams and senior client stakeholders.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
 </li>
 <li>
 3&ndash;6 years of experience building and shipping AI/ML solutions into production.
 </li>
 <li>
 Preferred certifications (nice to have): cloud AI certifications (Azure AI Engineer, AWS ML, Databricks GenAI Engineer).
 </li>
 </ul>
</div>

AI Engineer (6 months contract)

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: July 2026 
Location: Abu Dhabi (on-site)
Role Overview
We are seeking an experienced Full-Stack Engineer to own features from database to interface &mdash; designing APIs and data models, building responsive frontends, and shipping production code with tests, observability, and CI/CD. We value breadth across the stack with enough depth to make sound architectural calls.
The modern full-stack role increasingly includes integrating AI: calling LLM APIs, building RAG-backed and agentic features, and streaming model output into the UI. We work TypeScript-first, edge-aware, and type-safe end-to-end, with AI coding assistants as part of the everyday toolchain. 
Key Responsibilities<ul><li>
Build responsive, accessible interfaces with React/Next.js (or Vue/Svelte) and TypeScript.</li><li>
Develop and maintain backend services and APIs (REST, GraphQL, gRPC, or tRPC) with clean domain models.</li><li>
Design relational and non-relational data models, write performant queries, and manage migrations.</li><li>
Integrate AI features &mdash; call LLM APIs, build RAG and tool-using flows, and stream output into the UI.</li><li>
Implement authentication and authorization securely (OAuth/OIDC, JWT, managed auth providers).</li><li>
Write automated tests across the stack &mdash; unit, integration, and end-to-end.</li><li>
Own features end-to-end, from design through deployment, monitoring, and iteration.</li><li>
Optimize performance: rendering strategy (SSR/SSG/ISR/edge), caching, Core Web Vitals, and query tuning.</li></ul> 
Candidate Requirements<ul><li>
Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.</li><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.</li><li>
3+ years building and shipping production web apps across frontend and backend.</li><li>
Strong JavaScript/TypeScript and a modern frontend framework (React/Next.js, Vue, or Svelte).</li><li>
Backend experience in at least one of Node.js, Python, Go, Java, or .NET, with solid API design.</li><li>
Strong SQL and database design (PostgreSQL/MySQL) plus a NoSQL or caching store.</li><li>
Experience with Git, CI/CD, testing, and deploying to a cloud or serverless platform.</li><li>
A solid understanding of web fundamentals: HTTP, REST, auth, caching, security, and performance.</li><li>
Experience integrating third-party and/or AI APIs into applications.</li></ul> 
Nice to have:<ul><li>
AI-native product features (copilots, RAG search, agentic flows).</li><li>
Monorepo tooling and design systems.</li><li>
Real-time features (WebSockets, CRDTs, collaborative editing).</li><li>
Mobile or cross-platform experience (React Native, Expo).</li><li>
DevOps fluency &mdash; containers, IaC basics, owning your own deployments.</li></ul>
Tools &amp; Technologies You&rsquo;ll Work With:<ul><li>
Frontend: React, Next.js (App Router, RSC), Vue/Nuxt, Svelte/SvelteKit, Tailwind CSS, shadcn/ui.</li><li>
State &amp; data: TanStack Query, Zustand, Server Actions.</li><li>
Backend: Node.js, Bun; FastAPI/Django, Spring Boot, .NET, Go.</li><li>
APIs &amp; data: REST, GraphQL, tRPC, gRPC; PostgreSQL, MongoDB, Redis, pgvector; Prisma, Drizzle.</li><li>
AI integration: Vercel AI SDK, OpenAI/Anthropic/Gemini SDKs, RAG and embeddings.</li><li>
Auth &amp; testing: OAuth/OIDC, Auth.js, Clerk; Vitest, Jest, Playwright, Cypress.</li><li>
Build &amp; deploy: Vite, Turborepo/Nx, pnpm; Vercel, AWS/Azure/GCP, Docker, GitHub Actions; Sentry.</li></ul></div>

Full-Stack Engineer (front-end focused, 6 months contract)

<div>Contract duration: 6 months (with a potential extension) 
Engagement Type: Full-time 
Start date: August/September 2026 
Location: Abu Dhabi (on-site)
Role Overview
We are seeking an experienced DevOps Engineer to make software delivery fast, repeatable, and safe. You&rsquo;ll build and operate CI/CD pipelines, infrastructure-as-code, container platforms, and observability &mdash; cutting lead time and toil while improving reliability and security.
The discipline has shifted toward platform engineering: building internal developer platforms and golden paths so product teams can self-serve, with GitOps, policy-as-code, and supply-chain security built in. DevSecOps and FinOps are core parts of the job here, not afterthoughts. 
Key Responsibilities<ul><li>
Design and maintain CI/CD pipelines with automated testing, security scanning, and progressive delivery.</li><li>
Author infrastructure as code with Terraform/OpenTofu using reusable modules and clean environment promotion.</li><li>
Build and operate Kubernetes platforms &mdash; Helm/Kustomize packaging, autoscaling, and ingress.</li><li>
Implement GitOps with Argo CD or Flux so the desired state of infrastructure and apps lives in Git.</li><li>
Stand up observability: metrics, logs, traces, dashboards, SLOs, and alerting.</li><li>
Embed DevSecOps: SAST/DAST/SCA scanning, secrets management, image hardening, and software supply-chain security.</li><li>
Build internal developer platforms and golden paths that enable self-service for product teams.</li><li>
Manage cloud infrastructure with cost awareness, automate operational tasks, and improve DORA metrics; participate in on-call and run blameless post-mortems. 
 
Candidate Requirements</li><li>
Demonstrated experience delivering AI solutions in an enterprise or client-facing/consulting environment.</li><li>
Prior experience at management consulting firms and/or Big Tech is an advantage.</li><li>
Bachelor's or Master&rsquo;s degree in Computer Science, Engineering, Information Technology, or a related field.</li><li>
3+ years in DevOps, SRE, platform, or infrastructure engineering.</li><li>
Strong Linux fundamentals and scripting in Bash and Python (Go a plus).</li><li>
Production experience with a major cloud (AWS, Azure, or GCP).</li><li>
Hands-on experience running Docker and Kubernetes in production.</li><li>
Proficiency with infrastructure as code (Terraform/OpenTofu) and a CI/CD system (GitHub Actions, GitLab CI, or Jenkins).</li><li>
Solid grasp of networking, DNS, TLS, load balancing, and security fundamentals.</li><li>
Experience with monitoring/observability tooling and incident management.</li></ul> 
Nice to have:<ul><li>
Operating ML/AI infrastructure (GPU clusters, model serving, MLOps/LLMOps pipelines).</li><li>
Multi-cloud or hybrid-cloud architecture and migrations.</li><li>
Chaos engineering and advanced reliability practices.</li><li>
eBPF-based tooling (Cilium, Pixie) and custom Kubernetes operators.</li><li>
Certifications such as CKA/CKAD, a cloud architect cert, or Terraform Associate.</li></ul>
Tools &amp; Technologies You&rsquo;ll Work With:<ul><li>
Scripting: Bash, Python, Go; YAML, HCL.</li><li>
CI/CD: GitHub Actions, GitLab CI, Jenkins, Azure DevOps.</li><li>
IaC &amp; config: Terraform/OpenTofu, Pulumi, Ansible, Crossplane.</li><li>
Containers &amp; GitOps: Docker, Kubernetes, Helm, Kustomize, Argo CD, Flux, Argo Rollouts.</li><li>
Observability: Prometheus, Grafana, OpenTelemetry, Loki, ELK/OpenSearch, Datadog.</li><li>
DevSecOps: Trivy, Snyk, SonarQube, Checkov, OPA/Kyverno; Sigstore, SLSA, SBOMs.</li><li>
Secrets &amp; platform: HashiCorp Vault, External Secrets, OIDC workload identity; Backstage; AWS, Azure, GCP.</li></ul></div>

Join Contango and explore 10   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

Contango

About Company

Senior Data Scientist (6 months contract)

Senior Data Scientist (6 months contract)

DevOps Engineer (6 months contract)

Data Engineer (6 months contract)

AI Engineer (6 months contract)

Full-Stack Engineer (front-end focused, 6 months contract)

DevOps Engineer (6 months contract)