AI/ML Engineer

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.

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

5+ Years; 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.

Nice to have

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.