We are looking for a seasoned Lead Machine Learning Engineer to join our growing AI team. This role is pivotal in designing, building, and scaling robust machine learning systems and infrastructure. The ideal candidate will have a strong background in software engineering, MLOps, and production-level deployment of AI/ML solutions with strategic solution design using Azure or AWS. You will lead the development and deployment of robust, scalable ML solutions with a focus on NLP and Generative AI, while also establishing foundational MLOps practices and cloud architecture standards. You will work closely with both business and technical stakeholders to drive high-impact AI initiatives from experimentation to production.
Job Responsibilities:
- Design, build, and deploy scalable ML pipelines and services for model training, evaluation, deployment, and monitoring using Azure AI tools and infrastructure.
- Implement CI/CD for ML systems, using Azure DevOps for streamlined model development and deployment.
- Architect and implement scalable, secure, and cost-effective cloud-native solutions for NLP and Generative AI use cases.
- Collaborate with Data Scientists to productionize prototypes and optimize inference performance.
- Build and maintain data pipelines and feature stores in collaboration with Data Engineering.
- Evaluate and integrate open-source tools and cloud services (AWS, Azure) into the ML stack.
- Establish technical standards and best practices in collaboration with the Director of AI.
- Implement security measures for all AI deployments to guard against unauthorized access and potential breaches.
- Ensure AI models comply with ethics and compliance rules of the company.
- Translate experimental models into reliable, production-ready services, working with cross-functional teams (product, engineering, IT).
- Mentor junior engineers and contribute to growing the AI team.
- Document systems architecture, deployment strategies, and troubleshooting procedures.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Machine Learning, or a related field.
- Professional Experience:
- 5+ years of experience in ML engineering and AI solution architecture.
- 2+ years of hands-on experience with Azure or AWS services.
- Experience in finetuning and deploying large language models (LLMs) and generative models such as GPT, Llama, Mistral, etc., in production environments.
- Strong grasp of cloud infrastructure, especially Azure (e.g., Azure Functions, Azure Kubernetes, Azure AI Studio).
- Expertise in CI/CD and DevOps practices using Azure DevOps.
- Designing end-to-end ML pipelines using tools such as MLflow, Kubeflow, or Vertex AI.
- Working with cloud infrastructure (AWS/Azure), Docker, and Kubernetes.
Job-Specific Skills:
- Expert knowledge in Python language and ML frameworks like PyTorch, TensorFlow.
- Proficiency with orchestration tools (Airflow, Argo) and infrastructure as code (Azure, Ansible).
- Strong grasp of MLOps practices: model versioning, reproducibility, monitoring, logging.
- Experience with APIs, microservices, and scalable backend systems.
- Strong understanding of data structures, algorithms, and distributed computing.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills and ability to collaborate with diverse teams.
Location: Abu Dhabi Emirate, United Arab Emirates (On-site)
Job Type: Full-time
Language Requirements: No specific language requirements mentioned.
