ML Ops and DevOps Engineer at Deeplight

Abu Dhabi, Abu Dhabi, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

MLOps, DevOps, AWS, Azure, GitHub Actions, Terraform, CI/CD, SageMaker, EKS, LLMOps, Containerization, Data Engineering, Cloud Security, Generative AI, LLM Agents, Communication

Industry

IT Services and IT Consulting

Description

DeepLight AI is a specialist AI and data consultancy with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking. Our team combines deep expertise in data science, statistical modeling, AI/ML technologies, workflow automation, and systems integration with a practical understanding of complex business operations. The ML Ops and DevOps Engineer is a specialised technical role focused on bridging the gap between AI development and production-grade operations. You will design, deploy, and manage scalable AI solutions primarily across AWS and Azure environments, integrating Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps). By leveraging containerization, automated CI/CD pipelines, and robust data engineering, you will ensure that the Bank’s AI-driven applications—including Generative AI and LLM agents—are secure, efficient, compliant, and highly available. Your responsibilities as the MLOps and DevOps Engineer include: Design and deploy scalable AI infrastructure using GitHub Actions, EKS, ECS, and AWS Lambda; automate all resource provisioning via Terraform. Create and manage end-to-end CI/CD pipelines for model deployment, versioning, and lifecycle management using Azure DevOps and GitHub Actions. Deploy machine learning and large language models via Amazon SageMaker, EKS, and Azure ML Endpoints. Implement comprehensive monitoring for model performance and drift using Amazon CloudWatch, SageMaker Model Monitor, and OpenSearch. Integrate advanced services including Azure OpenAI, Azure Cognitive Services, and AWS Rekognition into enterprise workflows. Design robust data pipelines and real-time ingestion streams using AWS Glue, Amazon Redshift, Athena, and Apache Spark. Enforce cloud security through AWS WAF, Azure Key Vault, and RBAC, ensuring all deployments meet GDPR and ISO compliance standards. Supervise team activities and contribute to change initiatives in line with the Bank’s continuous improvement standards. 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 "why" behind your technical decisions, and effectively "selling" 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. What we need from you: A Bachelor’s degree in Computer Science, AI, Software Engineering, Statistics, Mathematics, or a related quantitative field. A minimum of 5 years in designing and deploying AI solutions in cloud environments, specifically within MLOps/LLMOps frameworks. Proficiency with GitHub Actions, Spark, Redshift, MongoDB, and Azure Purview. Deep hands-on expertise in AWS (SageMaker, EKS, S3) and Azure (OpenAI, Cognitive Services) ecosystems. Mastery of Terraform for multi-cloud resource automation. Proven experience in both MLOps (standard ML) and LLMOps (Large Language Models), focusing on fine-tuning, orchestration, and monitoring. Expertise in managing container images via ECR/ACR and deploying via Kubernetes (EKS/AKS). Strong understanding of secure networking (WAF, NSGs) and secret management. It would be beneficial if you also have: Azure and/or AWS certifications (e.g., AWS Certified Machine Learning - Specialty or Azure AI Engineer Associate) are highly preferred. Ability to seamlessly manage workloads that span across both AWS and Azure. Skill in communicating complex technical infrastructure requirements to non-technical business stakeholders. Adaptability in troubleshooting model drift and performance bottlenecks in real-time agentic workflows. Proactive identification of new tools (e.g., Vector DBs, orchestration frameworks) to improve deployment velocity. Benefits & Growth Opportunities: Competitive salary. Visa Sponsorship for the successful individual. Comprehensive health insurance for the successful individual. Professional development and certification support. Opportunity to work on cutting-edge AI projects. Career advancement opportunities in a rapidly growing AI company. 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.

Responsibilities

The engineer will design, deploy, and manage scalable AI infrastructure across AWS and Azure, focusing on integrating MLOps and LLMOps principles using tools like Terraform and GitHub Actions. Key duties include creating end-to-end CI/CD pipelines for model deployment, implementing monitoring, and enforcing cloud security standards.