We are seeking a highly skilled AI/ML Engineer to design, develop, and deploy scalable machine learning and deep learning solutions. The ideal candidate will have strong experience in computer vision, deep learning frameworks, and cloud-based ML deployment, along with solid software engineering and MLOps practices. You will work closely with cross-functional teams to build production-ready AI systems that deliver real business impact.
Experience: 5+ Years
Location: Bangalore
Timings: 2:30 to 11:30 PM IST
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Design, develop, and optimize machine learning and deep learning models using PyTorch.
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Build and deploy computer vision solutions for real-world use cases.
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Develop end-to-end ML pipelines, including data ingestion, preprocessing, training, validation, and deployment.
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Implement and maintain MLOps workflows for model versioning, monitoring, CI/CD, and retraining.
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Deploy and scale ML models on AWS cloud infrastructure.
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Work with large-scale datasets using Databricks and distributed computing frameworks.
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Collaborate with data scientists, product managers, and software engineers to translate business requirements into AI solutions.
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Ensure high code quality by following software engineering best practices (modular design, testing, documentation).
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Monitor model performance in production and continuously improve accuracy, efficiency, and reliability.
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Strong proficiency in Python for machine learning and software development.
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Hands-on experience with PyTorch for deep learning model development.
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Solid understanding of deep learning architectures (CNNs, transfer learning, etc.).
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Practical experience in computer vision applications.
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Experience working with Databricks and large-scale data processing.
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Strong knowledge of AWS services for ML deployment (EC2, S3, SageMaker, etc.).
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Experience with MLOps tools and practices (model deployment, monitoring, CI/CD).
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Good understanding of software engineering principles and production-grade system design.
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Experience deploying ML models in production environments.
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Familiarity with containerization tools such as Docker and orchestration platforms like Kubernetes.
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Exposure to real-time or batch inference systems.
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Experience working in agile or fast-paced development environments.
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Experience with optimization and performance tuning of ML models.
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Knowledge of data security and compliance in cloud environments.
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Experience with monitoring tools for ML model performance and drift detection.
