Design, develop, and optimize machine learning and deep learning models using PyTorch while building end-to-end ML pipelines. Collaborate with cross-functional teams to deploy scalable AI solutions on AWS and maintain MLOps workflows.
Job Title: AI/ML Engineer
Job Summary
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
Key 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.
Required Skills & Qualifications
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.
Preferred Qualifications
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.
Nice to Have
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.
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