We are seeking a highly skilled and experienced AI/ML Engineer to join our dynamic team. The ideal candidate will have a strong background in developing, deploying, and optimizing machine learning models, with hands-on experience in cutting-edge AI/ML frameworks and tools. You will work closely with cross-functional teams, including data engineering, to build scalable and efficient ML solutions that drive business value.

Key Responsibilities :

- Design, develop, and deploy robust AI/ML models tailored to solve complex business problems.

- Build and maintain machine learning pipelines for real-time and batch processing of data.

- Implement model optimization, monitoring, and troubleshooting strategies to ensure high performance in production environments.

- Collaborate closely with data engineering teams to integrate ML models with data platforms and streaming tools like Kafka.

- Utilize TensorFlow, PyTorch, and scikit-learn frameworks for model development.

- Ensure code quality, maintainability, and scalability in AI/ML solutions.

- Stay updated with the latest trends and best practices in AI/ML technologies and tools.

- Participate in architectural discussions and contribute to the continuous improvement of AI/ML infrastructure and workflows.

Required Skills & Qualifications :

- Minimum 7 years of hands-on experience in AI/ML model development and deployment.

- Strong programming skills in Python with experience in building and maintaining ML pipelines.

- Proficiency with AI/ML frameworks such as TensorFlow, PyTorch, and scikit-learn.

- Experience with real-time data processing tools, especially Kafka.

- Proven track record of productionizing, optimizing, and monitoring machine learning models at scale.

- Ability to collaborate effectively with data engineering and other cross-functional teams.

Preferred Skills (Good to Have) :

- Familiarity with object storage systems such as MinIO or AWS S3.

- Experience working with cloud-based AI platforms like AWS SageMaker or equivalent.

- Understanding of cloud infrastructure and deployment best practices for AI/ML workloads.