Machine Learning Engineer

Machine Learning Engineer

Role & responsibilities

• Design, develop, and optimize machine learning models for various business applications.
• Implement and enhance Generative AI technologies, including diffusion and GAN models.
• Write efficient, scalable, and well-documented Python code for ML pipelines.
• Collaborate with data engineers to preprocess and clean large datasets.
• Implement and optimize deep learning and traditional ML algorithms.
• Deploy, monitor, and maintain ML models in production environments.
• Utilize cloud platforms (AWS, GCP, or Azure) for scalable model deployment.
• Work with cross-functional teams to integrate ML solutions into existing products.
• Research and stay updated with the latest trends in AI/MLboth in the cloud and on-device.
• Optimize ML model performance and ensure compatibility across different platforms.
• Perform A/B testing and model performance evaluation.

Preferred candidate profile

• Bachelors or Masters degree in Computer Science, Data Science, AI, or a related field.
• 10+ years of hands-on experience in machine learning and data science.
• Strong programming expertise in

  Python

  , including experience with standard ML libraries such as TensorFlow and PyTorch.
• Proven experience with

  Generative AI models

  , such as diffusion and GAN models.
• Track record of delivering cloud-scale, data-driven products and services widely adopted by large customer bases.
• Experience with end-to-end ML model development and deployment.
• Advanced understanding of AI/ML, including ML frameworks.
• Proficiency in working with structured and unstructured data.
• Knowledge of MLOps best practices, including CI/CD for ML.
• Experience with cloud platforms (AWS SageMaker, Google Vertex AI, or Azure ML).
• Strong understanding of software engineering principles and version control (Git).

Preferred Candidate Skills:

• Experience with GPU optimization, including

  CUDA, Triton, TensorRT (TRT), and AOT

  .
• Experience converting models from various frameworks (PyTorch, TensorFlow) to other target formats for optimized performance across different platforms.
• Familiarity with Kubernetes and containerization for ML workloads.
• Hands-on experience in edge AI and federated learning.
• Strong knowledge of distributed computing and parallel processing.

Perks and benefits

• Competitive salary and stock options.
• Opportunity to work on cutting-edge AI solutions.
• Collaborative and innovative work environment.
• Professional growth and training support.
• Flexible working arrangements.