AI / ML Engineer

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: Pune
• 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.

• 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.

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.