Senior Machine Learning Engineer
7 days ago
About Wildnet Technologies
Wildnet Technologies, an award winning White Label Digital Marketing and IT Staff Augmentation Services Agency is a team of experienced professionals helping businesses and Google Partner Agencies achieve their goals by providing comprehensive range of High-Quality Digital Marketing Services and On-Demand Technology Resources. We have successfully delivered more than 12000 projects till date and today, our team comprises of more than 300+ professionals. We are headquartered in India and have client presence in the United States, Canada, Australia, and the United Kingdom
About the Job
Senior Machine Learning Engineering will lead the team of ML and Data Engineers, shaping Tesseract's vision for AI integration within Mixed Reality (MR) experiences. You will be responsible for building a high-performing team, defining the AI strategy, and driving the development and implementation of AI solutions across Tesseract's product portfolio.
What You’ll Be Doing
- Develop, deploy and maintain machine learning models, pipelines and workflows in production environment.
- Build and maintain machine learning infrastructure that is scalable, reliable and efficient.
- Collaborate with data scientists and software engineers to design and implement machine learning workflows.
- Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
- Experience of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platform.
- Continuously improve the performance, scalability and reliability of machine learning systems.
- Work with DevOps team to deploy and manage infrastructure for machine learning services.
- Experience with cloud-based AI platforms and tools (e.g., TensorFlow, PyTorch).
- Strong programming skills in languages such as Python, R, or Java.
- Experience of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms.
- Create and maintain technical documentation for machine learning infrastructure and workflows.
- Stay up to date with the latest developments in machine learning and cloud computing technologies.
What We Need To See
- Minimum 5 years of experience in software engineering, DevOps, or data engineering.
- Minimum 3 years of experience working with machine learning infrastructure and MLOps practices.
- Proven experience in deploying and managing ML models in production environments.
- Technical Expertise: Proficiency in Python and other relevant programming languages (e.g., Go, Java).
- DevOps Tools: Experience with DevOps tools such as Jenkins, GitLab CI, or CircleCI.
- Cloud Platforms: Strong experience with cloud infrastructure such as AWS, Google Cloud Platform (GCP), or Azure.
- Containerization: Experience with Docker and Kubernetes for deploying and managing microservices and models at scales.
- MLOps Tools: Familiarity with MLOps platforms like MLflow, Kubeflow, or TFX.
- Version Control: Strong knowledge of version control systems (e.g., Git) and CI/CD pipelines.
- Machine Learning Knowledge: Understanding of common machine learning algorithms and model development workflows.
- Experience with model monitoring and evaluation metrics such as accuracy, precision, recall, and AUC.
- Experience with model deployment frameworks (e.g., TensorFlow Serving, TorchServe, SageMaker).
- Collaboration Skills: Strong collaboration skills to work closely with data scientists, data engineers, and DevOps teams.
- Ability to translate complex technical requirements into practical, deployable solutions.
- Excellent problem-solving skills and attention to detail.
Benefits:
• Health, insurance.
• Paid time off and holidays.
• Professional development opportunities.
• Flexible working hours and remote work options.
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