ML Ops Engineer

Who we are

At LipDub AI we have been working for two years to build THE best in-house lip-syncing tool to break down language barriers in e-learning, advertising, and Hollywood. LipDub at its core modifies given footage to match any supplied audio track, allowing for use cases such as localization, ADR, scaled marketing, and many more. In the market today, we are the premier provider of accurately lip-synced high-fidelity video dubbing, and we’re not stopping here. LipDub algorithms are developed entirely in-house by some of the strongest researchers and leaders in computer graphics and generative modelling

(see: https://scholar.google.ca/citations?user=fAxws1sAAAAJ&hl=en and https://scholar.google.ca/citations?user=M9eTADwAAAAJ&hl=en).

Who we need

As an ML Ops Engineer at Lipdub AI, you will be responsible for developing and maintaining end-to-end ML pipelines that ensure our AI models' seamless deployment, monitoring, and optimization. You will collaborate with AI researchers, data scientists, and software engineers to deploy state-of-the-art ML models for real-time video and audio applications.

What you bring

• Design, develop, and optimize ML pipelines for training, validation, and inference.

• Automate deployment of deep learning and generative AI models for real-time applications.

• Implement model versioning, reproducibility, and rollbacks for seamless updates.

• Deploy and manage ML models on cloud platforms (AWS, GCP, Azure) using containerized solutions.

• Optimize real-time inference performance (TensorRT, ONNX Runtime, PyTorch).

• Work with GPU acceleration, distributed computing, and parallel processing for high-performance AI workloads.

• Fine-tune models to reduce latency and improve scalability in real-time AI-driven applications.

• Build and maintain CI/CD pipelines for ML models (GitHub Actions, Jenkins, ArgoCD).

• Automate model retraining, validation, and deployment to ensure continuous improvement.

• Develop monitoring solutions for model drift, data integrity, and inference performance.

• Ensure compliance with security, data privacy, and AI ethics standards.

We are looking for an engineer with the following experience and skills:

• 3+ years of experience in ML Ops, DevOps, or AI model deployment.

• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, ONNX).

• Experience deploying ML models using Docker, Kubernetes, and serverless architectures.

• Hands-on experience with ML pipeline tools (ArgoWorkflow, Kubeflow, MLflow, Airflow).

• Expertise in cloud platforms (AWS, GCP, or Azure) for AI/ML model hosting.

• Experience with GPU-based inference acceleration (CUDA, TensorRT, NVIDIA DeepStream).

• Proficiency in CI/CD workflows and automated testing for ML models.

• Solid understanding of real-time inference optimization and scalable ML infrastructure.

• Excellent Technical Judgment: You can design and implement elegant and clean solutions that meet the requirements of today while allowing for growth tomorrow. You know how to pick the right tool for the job.

• Strong Automation Focus: You seek to script and automate as much computing as possible. 

• Proven understanding of distributed systems and computing architectures.

• Motivation: You are self-driven and work well independently.

• You have working experience with Kubernetes, docker or microservices in general.

• BS or MS in Computer Science or equivalent work experience.

Nice to have:

• Some experience with CUDA Programming.

• Experience working with LLMs and generative AI models in production.

• Basic networking knowledge

• Knowledge of distributed computing frameworks (Ray, Horovod, Spark).

• Experience with edge AI deployment (Triton Inference Server, TFLite, CoreML).

What happens next 

After we receive your application, it will be reviewed by the Talent Acquisition team and the Hiring Manager. We will then invite successful candidates to subsequent rounds of interviews with the team, which will be conducted via Google Meet.

We are an equal opportunity employer that values diversity and does not discriminate on any protected characteristic defined by applicable law. We are committed to providing reasonable employment accommodations per the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act.

We will also look to provide reasonable accommodations as required for applicants to participate in the application and interview processes. Please let us know if you require any accommodation(s). We can be reached via the Contact Us page on our company website, or via phone at +1 (416) 840-5556. Please note that we cannot accept general applications via any of these contact methods; they are specifically for providing support and/or accommodations to those who require such assistance.