mlops engineer

Deeplight

<div>DeepLight AI is a specialist AI and data consultancy dedicated to transforming the regional corporate landscape through bespoke, high-impact intelligent systems. Based in the UAE, we partner with organizations across diverse sectors&mdash;with a deep-rooted expertise in Financial Services and Banking&mdash;to bridge the gap between complex data and actionable business strategy.
At DeepLight, we don't believe in &quot;off-the-shelf&quot; fixes. We deliver tailored AI solutions designed to integrate seamlessly into existing enterprise architectures, ensuring that innovation is both scalable and secure. From building robust data foundations to deploying sophisticated AI platforms, we empower our clients to lead in an increasingly automated world.
As a Senior Full Stack AI Engineer, you will be the ultimate bridge between advanced data science and enterprise-scale software architecture. This role is designed for a rare breed of engineer: someone who possesses the deep mathematical and modeling background required to design advanced AI and Generative AI systems, paired with the full-stack infrastructure capabilities needed to deploy, containerize, scale, and monitor them across hybrid, cloud, and on-premise environments.
Operating within complex client landscapes&mdash;such as premier banking and financial institutions (e.g., ADCB)&mdash;you will own the full engineering lifecycle of AI applications. You will design secure, highly performant systems, orchestrate massive data pipelines, optimize cloud spend, and establish robust MLOps practices that guarantee production stability.
Key ResponsibilitiesEnd-to-End AI Architecture &amp; Full-Stack Design<ul><li>
Architect end-to-end, highly secure AI and Generative AI systems capable of running seamlessly across both secure on-premise data centers and public cloud infrastructures (Azure/AWS).</li><li>
Design, build, and optimize high-throughput, secure APIs to serve complex AI models and agentic workflows to consuming client applications.</li><li>
Seamlessly integrate proprietary cloud foundation models (GPT, Claude, Gemini) and fine-tuned open-source models into unified software ecosystems.</li></ul>
Deep Learning &amp; Advanced AI Engineering<ul><li>
Leverage a deep theoretical understanding of Transformers, PyTorch, and TensorFlow to implement, evaluate, and optimize deep learning models across NLP and Computer Vision domains.</li><li>
Design scalable knowledge retrieval frameworks using embedding models and enterprise-grade Vector Databases (e.g., Azure DocumentDB, Elasticsearch, Faiss).</li><li>
Build and evaluate robust systematic prompting frameworks and compile complex models using ONNX for optimized, low-latency production inference.</li></ul>
Infrastructure, MLOps &amp; FinOps<ul><li>
Own the containerization and scaling of AI services utilizing Docker and Kubernetes clusters across development and production environments.</li><li>
Build and automate robust MLOps continuous integration and deployment pipelines to track model lineage, versions, evaluations, and production drift.</li><li>
Implement strict FinOps practices to track, monitor, and radically optimize token usage, cloud compute consumption, and inferencing costs.</li><li>
Architect and optimize large-scale data processing systems using modern Big Data tools to feed raw information into AI training and embedding workflows.</li></ul>
As an AI consultancy, our greatest asset is the expertise of our people.
 
While technical mastery is the foundation of what we do, the ability to bridge the gap between complex data science and actionable business value is what defines your success with Deeplight.
 
We're looking for individuals who are not only world-class in their fields of specialism, but also compelling communicators and persuasive advocates for their own skills.
 
You will be the face of our firm, tasked with building trust, articulating the &quot;why&quot; behind your technical decisions, and effectively &quot;selling&quot; your vision to high-level stakeholders.
 
If you thrive on the challenge of presenting cutting-edge solutions as much as you do on building them, you will fit right in. 
Requirements 
We need you to have:<ul><li>
Extensive experience designing, launching, and managing containerized AI/ML or data-intensive applications in highly regulated enterprise environments.</li><li>
A proven track record applying deep learning models across both Natural Language Processing (NLP) and Computer Vision (CV) fields.</li><li>
Practical experience implementing production-grade model monitoring frameworks and optimizing complex cloud/token expenditure.</li><li>
Deep operational command of Docker and enterprise Kubernetes infrastructure for running distributed AI applications.</li><li>
Expert-level knowledge of Azure (or alternative major clouds) alongside a strong architectural grasp of on-premise deployment constraints, data security, and network topologies.</li><li>
Strong proficiency in Big Data toolsets and modern API gateway designs.</li><li>
A rigorous, foundational grasp of transformer architectures, embedding mechanics, and deep learning implementations via PyTorch/TensorFlow.</li><li>
High proficiency across SQL, NoSQL, and Vector Database engines.</li><li>
&quot;Perfect-tier&quot; software engineering skills in Python and advanced data processing techniques.</li></ul>
It would also be great if you have:<ul><li>
An MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a highly relevant quantitative discipline.</li><li>
Deep domain experience within banking, understanding strict data residency laws, financial compliance protocols, and legacy core banking interactions.</li><li>
Practical experience with agentic frameworks (e.g., LangChain, LlamaIndex, AutoGen) and modern cloud MLOps suites (e.g., Azure AI Studio, Kubeflow).</li></ul> 
Benefits 
The benefits you'll enjoy as part of this role include: 
<ul><li>
Competitive salary
</li><li>Comprehensive personal health insurance
</li><li>Visa Sponsorship for the successful individual</li><li>
Professional development and certification support</li><li>
Subscription reimbursement relating to your role</li><li>
Opportunity to work on cutting-edge AI projects</li><li>
Monthly Employee Incentive program</li><li>
Career advancement opportunities in a rapidly growing AI company</li></ul> 
This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients. 
 
At DeepLight AI, we recognise that diversity drives innovation. We are committed to fostering an inclusive environment where individuals with different thinking styles can thrive and contribute their unique strengths to our specialised AI and data solutions.
Our goal is to ensure our application and interview process is accessible, predictable, and fair for all candidates.
If you require any specific adjustments to the application process, or if you require any reasonable adjustments should you be successful in being processed to the interview stage, please do let us know. This information will be kept strictly confidential and will not impact hiring decisions.</div>

Senior Full Stack Engineer

Emirates Nuclear Energy

<div></div><div><div><div><div><div>Posting Date: 7 Jul 2026
</div></div></div></div><div><div><div><div>Requisition ID: 3584
</div></div></div></div><div><div><div><div>Location: HQ - Abu Dhabi
</div></div></div></div><div><div><div><div>Posting Status: Active Recruitment
</div></div></div></div><div><div><div><div><div><div><div><h2 class="jobSectionHeader">Job Purpose</h2>
</div><div>The Data Science Expert leads and develops high-performing teams of data scientists and data engineers to deliver advanced analytics, AI, and predictive modeling solutions for ENEC&rsquo;s nuclear operations. This role ensures technical excellence, compliance with nuclear safety standards, and the delivery of actionable insights that optimize plant performance, fuel cycle management, and operational reliability. The Expert acts as a strategic advisor, technical authority, and mentor, driving innovation and embedding analytics into the core of ENEC&rsquo;s mission.
</div></div><div><div><h2 class="jobSectionHeader">Key Activities, Responsibility &amp; Accountability</h2>
</div><div><div>Responsibilities
 
<ul><li>Strategic Leadership:
</li></ul>Drive the data science and analytics strategy for nuclear operations, ensuring alignment with ENEC&rsquo;s safety, reliability, and business objectives.
 
<ul><li>Technical Excellence:
</li></ul>Oversee the design, development, and deployment of advanced models for reactor performance, fuel cycle optimization, outage prediction, and anomaly detection.
 
<ul><li>Team Leadership:
</li></ul>Lead, mentor, and develop multidisciplinary teams of data scientists and engineers, fostering a culture of innovation, collaboration, and continuous improvement.
 
<ul><li>Stakeholder Engagement:
</li></ul>Collaborate with plant, fuel, engineering, and regulatory teams to embed analytics into operational workflows and decision-making.
 
<ul><li>Governance &amp; Compliance:
</li></ul>Ensure all analytics activities adhere to nuclear safety culture, regulatory requirements, and robust data governance.
 
<ul><li>Operational Excellence:
</li></ul>Monitor and improve the impact of analytics on plant reliability, fuel efficiency, and business outcomes.
 
<ul><li>Expert Knowledge Application:
</li></ul>Apply advanced expertise in statistical modeling, machine learning, and AI to develop solutions for complex nuclear analytics challenges, ensuring technical rigor and industry compliance.
 
<ul><li>Complex Problem Solving:
</li></ul>Lead the resolution of ambiguous and high-impact analytics issues by designing innovative approaches, conducting root cause analysis, and implementing corrective/preventive actions to enhance operational reliability.
 
<ul><li>Professional Know-How:
</li></ul>Utilize deep technical know-how to architect, validate, and deploy digital twins, predictive maintenance models, and fuel cycle optimization tools, ensuring best practices in MLOps and automation.</div>
</div></div><div><div><h2 class="jobSectionHeader">Responsibilities &amp; Accountabilities (contd.)</h2>
</div><div>Responsibilities and Accountabilities:
<ul><li>Define, communicate, and continuously refine the analytics vision and multi-year roadmap for nuclear operations.
</li><li>Advise executive leadership on leveraging AI and advanced analytics for plant optimization, fuel cycle management, and operational excellence.
</li><li>Lead the development of business cases for analytics investments focused on nuclear plant performance, predictive maintenance, and fuel efficiency.
</li><li>Align analytics initiatives with ENEC&rsquo;s digital transformation, safety culture, and national mission.
</li><li>Monitor industry trends and emerging technologies to ensure ENEC remains at the forefront of nuclear analytics.
</li></ul>Responsibilities and Accountabilities:
<ul><li>Direct the design, development, and deployment of advanced statistical, machine learning, and AI models for nuclear plant operations, fuel cycle analysis, and safety-critical predictions.
</li><li>Oversee the development and validation of digital twins, predictive maintenance models, and fuel consumption forecasting tools.
</li><li>Ensure all models are validated against nuclear industry standards, regulatory requirements, and plant-specific constraints.
</li><li>Lead the adoption of MLOps best practices for continuous monitoring, retraining, and governance of models deployed in nuclear environments.
</li><li>Drive the adoption of automation, CI/CD pipelines, and advanced monitoring for analytics operations.
</li><li>Lead technical reviews, audits, and assurance activities to maintain high standards of quality, reliability, and compliance.
</li><li>Ensure robust documentation, explainability, and auditability of all models and analytics solutions</li></ul>
</div></div><div><div><h2 class="jobSectionHeader">Professional Certifications</h2>
</div><div>Relevant certifications in AI/ML, MLOps, and Data Governance.
</div></div><div><div><h2 class="jobSectionHeader">Qualifications</h2>
</div><div>Min - Master&rsquo;s degree in data science, computer science, engineering, or related discipline. Proven experience leading data science teams in regulated environments
Pref - PhD or equivalent. Experience in nuclear, energy, or highly regulated sectors.
</div></div><div><div><h2 class="jobSectionHeader">Experience</h2>
</div><div>8 years of relevent experience</div></div></div></div></div></div></div></div>

Data Science Expert

ENEC Operations

<div>
 Job Title: Data Science Expert
 
 
 Posting Date: 7 Jul 2026
 
 
 Requisition ID: 3584
 
 
 Location: HQ - Abu Dhabi
 
 
 Posting Status: Active Recruitment
 
 
 
 Job Purpose
 
 
 
 The Data Science Expert leads and develops high-performing teams of data scientists and data engineers to deliver advanced analytics, AI, and predictive modeling solutions for ENEC&rsquo;s nuclear operations. This role ensures technical excellence, compliance with nuclear safety standards, and the delivery of actionable insights that optimize plant performance, fuel cycle management, and operational reliability. The Expert acts as a strategic advisor, technical authority, and mentor, driving innovation and embedding analytics into the core of ENEC&rsquo;s mission.
 
 
 Key Activities, Responsibility &amp; Accountability
 
 
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Strategic Leadership:
 
 
 </li>
 </ul>
 Drive the data science and analytics strategy for nuclear operations, ensuring alignment with ENEC&rsquo;s safety, reliability, and business objectives.
 
 
 <ul>
 <li>
 Technical Excellence:
 
 
 </li>
 </ul>
 Oversee the design, development, and deployment of advanced models for reactor performance, fuel cycle optimization, outage prediction, and anomaly detection.
 
 
 <ul>
 <li>
 Team Leadership:
 
 
 </li>
 </ul>
 Lead, mentor, and develop multidisciplinary teams of data scientists and engineers, fostering a culture of innovation, collaboration, and continuous improvement.
 
 
 <ul>
 <li>
 Stakeholder Engagement:
 
 
 </li>
 </ul>
 Collaborate with plant, fuel, engineering, and regulatory teams to embed analytics into operational workflows and decision-making.
 
 
 <ul>
 <li>
 Governance &amp; Compliance:
 
 
 </li>
 </ul>
 Ensure all analytics activities adhere to nuclear safety culture, regulatory requirements, and robust data governance.
 
 
 <ul>
 <li>
 Operational Excellence:
 
 
 </li>
 </ul>
 Monitor and improve the impact of analytics on plant reliability, fuel efficiency, and business outcomes.
 
 
 <ul>
 <li>
 Expert Knowledge Application:
 
 
 </li>
 </ul>
 Apply advanced expertise in statistical modeling, machine learning, and AI to develop solutions for complex nuclear analytics challenges, ensuring technical rigor and industry compliance.
 
 
 <ul>
 <li>
 Complex Problem Solving:
 
 
 </li>
 </ul>
 Lead the resolution of ambiguous and high-impact analytics issues by designing innovative approaches, conducting root cause analysis, and implementing corrective/preventive actions to enhance operational reliability.
 
 
 <ul>
 <li>
 Professional Know-How:
 
 
 </li>
 </ul>
 Utilize deep technical know-how to architect, validate, and deploy digital twins, predictive maintenance models, and fuel cycle optimization tools, ensuring best practices in MLOps and automation.
 
 
 
 Responsibilities &amp; Accountabilities (contd.)
 
 
 
 Responsibilities and Accountabilities:
 
 
 <ul>
 <li>
 Define, communicate, and continuously refine the analytics vision and multi-year roadmap for nuclear operations.
 </li>
 <li>
 Advise executive leadership on leveraging AI and advanced analytics for plant optimization, fuel cycle management, and operational excellence.
 </li>
 <li>
 Lead the development of business cases for analytics investments focused on nuclear plant performance, predictive maintenance, and fuel efficiency.
 </li>
 <li>
 Align analytics initiatives with ENEC&rsquo;s digital transformation, safety culture, and national mission.
 </li>
 <li>
 Monitor industry trends and emerging technologies to ensure ENEC remains at the forefront of nuclear analytics.
 
 
 </li>
 </ul>
 
 Responsibilities And Accountabilities
 
 
 
 <ul>
 <li>
 Direct the design, development, and deployment of advanced statistical, machine learning, and AI models for nuclear plant operations, fuel cycle analysis, and safety-critical predictions.
 </li>
 <li>
 Oversee the development and validation of digital twins, predictive maintenance models, and fuel consumption forecasting tools.
 </li>
 <li>
 Ensure all models are validated against nuclear industry standards, regulatory requirements, and plant-specific constraints.
 </li>
 <li>
 Lead the adoption of MLOps best practices for continuous monitoring, retraining, and governance of models deployed in nuclear environments.
 </li>
 <li>
 Drive the adoption of automation, CI/CD pipelines, and advanced monitoring for analytics operations.
 </li>
 <li>
 Lead technical reviews, audits, and assurance activities to maintain high standards of quality, reliability, and compliance.
 </li>
 <li>
 Ensure robust documentation, explainability, and auditability of all models and analytics solutions
 
 
 </li>
 </ul>
 Professional Certifications
 
 
 Relevant certifications in AI/ML, MLOps, and Data Governance.
 
 
 
 Qualifications
 
 
 
 Min - Master&rsquo;s degree in data science, computer science, engineering, or related discipline. Proven experience leading data science teams in regulated environments
 
 
 Pref - PhD or equivalent. Experience in nuclear, energy, or highly regulated sectors.
 
 
 
 Experience
 
 
 
 8 years of relevent experience
</div>

<div>DeepLight AI is a specialist AI and data consultancy dedicated to transforming the regional corporate landscape through bespoke, high-impact intelligent systems. Based in the UAE, we partner with organizations across diverse sectors&mdash;with a deep-rooted expertise in Financial Services and Banking&mdash;to bridge the gap between complex data and actionable business strategy.
At DeepLight, we don't believe in &quot;off-the-shelf&quot; fixes. We deliver tailored AI solutions designed to integrate seamlessly into existing enterprise architectures, ensuring that innovation is both scalable and secure. From building robust data foundations to deploying sophisticated AI platforms, we empower our clients to lead in an increasingly automated world.
As a Senior AI Engineer, you will drive the design, development, and deployment of production-grade Artificial Intelligence and Generative AI solutions. This role sits at the intersection of cutting-edge research and enterprise-scale application engineering, specifically focused on delivering high-impact, secure, and performant intelligent systems for premier clients in regulated spaces like financial services and banking.
You will act as a technical pillar within the AI squad&mdash;collaborating with Data Architects, UI Engineers, and delivery leads to transform complex business requirements into robust, production-ready AI agents and pipelines. This role demands exceptional technical mastery, a deep commitment to architectural integrity, and a proactive approach to solving ambiguous data challenges.
Key ResponsibilitiesGenerative &amp; Agentic AI Engineering<ul><li>
Cognitive Systems Architecture: Design, build, and optimize enterprise-grade Generative AI applications and multi-agent frameworks capable of complex reasoning and autonomous task execution.</li><li>
Knowledge Retrieval Systems: Architect and maintain scalable Retrieval-Augmented Generation (RAG) pipelines utilizing advanced vector databases and semantic search strategies.</li><li>
Advanced Prompt Engineering: Develop and systematically evaluate advanced prompting techniques, guardrails, and context-window management strategies to ensure predictable, secure model behavior.</li></ul>
Deep Learning &amp; Model Optimization<ul><li>
Core Modeling &amp; Execution: Train, fine-tune, and deploy deep learning models across both Natural Language Processing (NLP) and Computer Vision (CV) domains.</li><li>
Open-Source &amp; Cloud Integration: Evaluate and deploy both proprietary cloud models (e.g., GPT, Claude) and open-source foundation models, balancing performance, latency, and cost.</li><li>
Edge &amp; Inference Optimization: Convert and optimize models for efficient production deployment and runtime inference using formats like ONNX.</li></ul>
Data &amp; MLOps Infrastructure<ul><li>
Data Processing Pipelines: Architect and refine complex, highly performant data preprocessing and feature engineering pipelines for unstructured and structured data.</li><li>
Database Governance: Design and optimize high-throughput queries across relational SQL databases and specialized NoSQL/Vector data stores.</li><li>
Rigorous Evaluation: Establish robust, data-driven model evaluation frameworks to continuously benchmark accuracy, drift, latency, and safety in production.</li></ul>
As an AI consultancy, our greatest asset is the expertise of our people.
 
While technical mastery is the foundation of what we do, the ability to bridge the gap between complex data science and actionable business value is what defines your success with Deeplight.
 
We're looking for individuals who are not only world-class in their fields of specialism, but also compelling communicators and persuasive advocates for their own skills.
 
You will be the face of our firm, tasked with building trust, articulating the &quot;why&quot; behind your technical decisions, and effectively &quot;selling&quot; your vision to high-level stakeholders.
 
If you thrive on the challenge of presenting cutting-edge solutions as much as you do on building them, you will fit right in. 
Requirements 
We need you to have:<ul><li>
A proven track record of designing, evaluating, and operating AI/ML models in high-availability enterprise or production environments.</li><li>
Demonstrated professional or research experience spanning both Natural Language Processing (NLP) and Computer Vision (CV).</li><li>
Hands-on experience leveraging enterprise cloud AI infrastructures (such as Azure AI Studio or AWS Bedrock) to deploy foundation models.</li></ul>
AI, Modeling &amp; Frameworks<ul><li>
Deep conceptual and practical understanding of Large Language Models (LLMs), agentic workflows, function calling, and embedding models.</li><li>
Expert proficiency in PyTorch and TensorFlow for building, training, and troubleshooting deep neural networks.</li><li>
A practical understanding of model compilation, quantization, and deployment optimization via ONNX runtime.</li><li>
High proficiency in configuring and querying Vector Databases (e.g., Azure DocumentDB, Elasticsearch, Faiss) alongside extensive knowledge of enterprise SQL and NoSQL architectures.</li></ul>
Software Engineering &amp; Core Tools<ul><li>
Perfect-tier coding skills in Python, with a command of asynchronous programming, design patterns, and clean, modular code architecture.</li><li>
Advanced capabilities in processing massive datasets utilizing standard data ecosystems (e.g., Pandas, NumPy, Polars, or PySpark)</li></ul>
It would also be great if you had: 
<ul><li>
An MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a highly related quantitative field.</li><li>
Prior experience engineering AI solutions within banking, fintech, or highly regulated corporate environments (e.g., familiarity with data privacy, compliance, and secure financial data models).</li><li>
Familiarity with framework libraries such as LangChain, LlamaIndex, or AutoGen for agentic workflow construction.</li></ul> 

Benefits 
The benefits you'll enjoy as part of this role include: 
<ul><li>
Competitive salary
</li><li>Comprehensive personal health insurance
</li><li>Visa Sponsorship for the successful individual</li><li>
Professional development and certification support</li><li>
Subscription reimbursement relating to your role</li><li>
Opportunity to work on cutting-edge AI projects</li><li>
Monthly Employee Incentive program</li><li>
Career advancement opportunities in a rapidly growing AI company</li></ul> 
This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients. 
 
At DeepLight AI, we recognise that diversity drives innovation. We are committed to fostering an inclusive environment where individuals with different thinking styles can thrive and contribute their unique strengths to our specialised AI and data solutions.
Our goal is to ensure our application and interview process is accessible, predictable, and fair for all candidates.
If you require any specific adjustments to the application process, or if you require any reasonable adjustments should you be successful in being processed to the interview stage, please do let us know. This information will be kept strictly confidential and will not impact hiring decisions.</div>

Senior AI Engineer

Klanik

<div>
 
 We are currently looking for a talented
 
 Data Scientist &ndash;Team Lead &amp; Data Scientist
 
 to
 
 
 our team in
 
 Abu Dhabi
 
 !!!
 
 
 
 
 
 
 Requirements:
 
 
 
 
 
 <ul>
 <li>
 Computer vision (object detection, tracking, re-ID, action recognition, segmentation);
 </li>
 <li>
 YOLO, ViT / Transformer models, RF-DETR;
 </li>
 <li>
 VLMs &amp; fine-tuning; &bull; optimisation (TensorRT, ONNX, OpenVINO);
 </li>
 <li>
 Python, PyTorch / TensorFlow;
 </li>
 <li>
 Full MLOps lifecycle; edge-AI constraints (latency, FPS, GPU);
 </li>
 <li>
 Multi-camera tracking;
 </li>
 <li>
 Team leadership, code review &amp; mentoring;
 </li>
 <li>
 Stakeholder communication &amp; solution architecture.
 </li>
 <li>
 CV expertise (detection, tracking, segmentation)
 </li>
 <li>
 Hands-on with YOLO, RF-DETR and similar models
 </li>
 <li>
 Experience in fine-tuning Vision-Language Models (VLMs)
 </li>
 <li>
 Quantization Experience
 </li>
 <li>
 Data preprocessing, augmentation &amp; dataset curation
 </li>
 <li>
 Model training, tuning, evaluation (mAP, precision/recall)
 </li>
 <li>
 Experience with video datasets &amp; annotation feedback loop
 </li>
 <li>
 Experiment tracking (MLflow/W&amp;B)
 </li>
 <li>
 Model optimization basics (TensorRT, ONNX)
 </li>
 <li>
 Understanding of real-world deployment challenges
 </li>
 </ul>
 
 
 
 
 
 Educational
 
 : Computer science or engineering (preferred)
 
 
 
 
 <ul>
 <li>
 3-7 years of hands-on experience in AI/ML applications
 </li>
 <li>
 8-15 years leading and coaching teams, demonstrable track record of projects delivered, bringing value to its recipients
 </li>
 </ul>
 
 
 
 
 You are looking for a great opportunity to be part of an amazing challenge. This job is for you!!!
 
 
 
 
 
 
 
 About KLANIK:
 
 
 
 
 
 
 
 Klanik is an IT consulting company providing solutions to some of the world&rsquo;s largest industrial and services groups for 10 years.
 
 
 We support our clients with the creation and development of their new products and services around 4 main business lines:
 
 
 ➜ Information Technology and Systems (IT &amp; IS)
 
 
 ➜ DevOps and Cloud
 
 
 ➜ Big Data and AI
 
 
 ➜ Cybersecurity
 
 
 
 
 
 Klanik is a community of +500 passionate experts empowering clients in 6 countries throughout Europe, Middle East and the Americas.
 
 
 Our Team is dedicated to offer our global clients project support while guaranteeing a consistent level of service alongside a commitment to excellence and strong core values.
 
 
 We&rsquo;re always looking for smart, talented, driven, down-to-earth, fun-to-work-with people who want to make a positive and meaningful impact!
 
 
 
 
 
 
 
 Being a &ldquo;KLANIK&rdquo;
 
 
 
 
 That means being an out-of-the-box thinker, a precursor who is keen to create, innovate and collaborate in an environment where you can enjoy being yourself and valued for your ideas. It&rsquo;s being part of a community of experts who have a passion for and stay abreast of the latest technologies.
 
 
 Do you feel like a technology pioneer, full of creative ideas, supported by strong know-how and expertise in the latest technologies? We believe you could be a good fit for our culture!
 
 
 
 
 
 
 
 Our culture is our pride
 
 
 
 
 At Klanik, we believe that we are stronger together than we are alone. Trust, ethics, respect, and transparency are deeply fixed in our culture, alongside the pride in a job well done.
 
 
 Our community motto is that people give the best of themselves when they feel listened to, respected, and empowered to develop themselves. Therefore, the better we treat our people, the bigger the success they will achieve. We apply those values on a day-to-day basis among our teams, with our clients, and even with our competitors.
 
 
 Being part of the Klanik community means relying on each other, learning from each other, and growing all together. Our community fosters creativity, innovation, and development within our teams to tackle more and more complex challenges and deliver the best solutions to our clients.
 
</div>

Data Scientist – Team Lead

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:<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>

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:
 </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>

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