Key skills
PythonAIMachine LearningMLDeep LearningComputer VisionPyTorchMLOpsDatabricksAWSKubernetesDockerEC2S3SageMakerAgileCI/CD
About this role
Codvo.ai is seeking a highly skilled AI/ML Engineer to design, develop, and deploy scalable machine learning and deep learning solutions. The ideal candidate will work closely with cross-functional teams to build production-ready AI systems that deliver real business impact.
Responsibilities:
- Design, develop, and optimize machine learning and deep learning models using PyTorch
- Build and deploy computer vision solutions for real-world use cases
- Develop end-to-end ML pipelines, including data ingestion, preprocessing, training, validation, and deployment
- Implement and maintain MLOps workflows for model versioning, monitoring, CI/CD, and retraining
- Deploy and scale ML models on AWS cloud infrastructure
- Work with large-scale datasets using Databricks and distributed computing frameworks
- Collaborate with data scientists, product managers, and software engineers to translate business requirements into AI solutions
- Ensure high code quality by following software engineering best practices (modular design, testing, documentation)
- Monitor model performance in production and continuously improve accuracy, efficiency, and reliability
Requirements:
- Strong proficiency in Python for machine learning and software development
- Hands-on experience with PyTorch for deep learning model development
- Solid understanding of deep learning architectures (CNNs, transfer learning, etc.)
- Practical experience in computer vision applications
- Experience working with Databricks and large-scale data processing
- Strong knowledge of AWS services for ML deployment (EC2, S3, SageMaker, etc.)
- Experience with MLOps tools and practices (model deployment, monitoring, CI/CD)
- Good understanding of software engineering principles and production-grade system design
- Experience deploying ML models in production environments
- Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes
- Exposure to real-time or batch inference systems
- Experience working in agile or fast-paced development environments
- Experience with optimization and performance tuning of ML models
- Knowledge of data security and compliance in cloud environments
- Experience with monitoring tools for ML model performance and drift detection