Senior MLOps Engineer

Senior MLOps Engineer – Real-Time AI & Video Applications (100% Remote)

Job Type: Full-time

We're hiring for an impressive AI company who are focussed on real-time AI and Video Applications. Their team is made up of leading experts in computer graphics and generative modeling, and they are on a rapid growth trajectory. We're looking for experienced MLOps Engineers that want to work on real-time AI applications that are shaping the future of media.

The Role

We’re looking for a talented MLOps Engineer to build and maintain robust machine learning pipelines and infrastructure. You’ll be working closely with AI researchers, data scientists, and software engineers to deploy state-of-the-art models into production, optimize real-time inference, and ensure systems scale effectively.

What You’ll Do

• Design and optimize ML pipelines for training, validation, and inference
• Automate deployment of deep learning and generative models for real-time use
• Implement versioning, reproducibility, and rollback capabilities
• Deploy and manage containerized ML solutions on cloud platforms (AWS, GCP, Azure)
• Optimize model performance using TensorRT, ONNX Runtime, and PyTorch
• Work with GPUs, distributed computing, and parallel processing to power AI workloads
• Build and maintain CI/CD pipelines using tools like GitHub Actions, Jenkins, ArgoCD
• Automate model retraining, monitoring, and performance tracking
• Ensure compliance with privacy, security, and AI ethics standards

What You Bring

• 3+ years of experience in MLOps, DevOps, or AI model deployment
• Strong skills in Python and frameworks like TensorFlow, PyTorch, ONNX
• Proficiency with Docker, Kubernetes, and serverless architectures
• Hands-on experience with ML tools (ArgoWorkflow, Kubeflow, MLflow, Airflow)
• Experience deploying and optimizing GPU-based inference (CUDA, TensorRT, DeepStream)
• Solid grasp of CI/CD practices and scalable ML infrastructure
• Passion for automation and clean, maintainable system design
• Strong understanding of distributed systems
• Bachelor’s or Master’s in Computer Science or equivalent work experience

Bonus Skills

• Experience with CUDA programming
• Exposure to LLMs and generative AI in production
• Familiarity with distributed computing (Ray, Horovod, Spark)
• Edge AI deployment experience (Triton Inference Server, TFLite, CoreML)
• Basic networking knowledge

Please apply now for more details and next steps

We look forward to hearing from you