Experience
5 - 10 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Ai Ml Engineer
Job Type
Not specified
Job Description
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.
- 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.
- 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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