Job Summary :

We are seeking a highly skilled AI/ML Engineer with 5+ Years of experience 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.

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.

Domain : Med Tech

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.