Job Title: MLOps and Gen AI Engineer

 

Job Description:

We are seeking an MLOps Engineer with 3+ years of relevant experience in designing and deploying Machine Learning solutions to join our team. In this role, you will be responsible for supporting the end-to-end lifecycle of Machine Learning and Generative AI models, from development to deployment.

 

Responsibilities:

• MLOps Pipeline Development: Design, implement, and maintain robust MLOps pipelines to facilitate seamless model training, evaluation, deployment, and monitoring.
• Cloud Infrastructure Management: Utilize your expertise in Azure, GCP, or AWS to manage and optimize cloud infrastructure, ensuring efficient and scalable machine learning workflows.
• Python Development: Leverage your python skills to develop and optimize code for machine learning workflows, data processing, and automation scripts.
• Machine Learning Support: Provide support for machine learning tasks, including data pre-processing, feature engineering, model training, and evaluation.
• Generative AI Understanding: Understanding of generative AI concepts and frameworks such as Langchain, LLamaIndex, or OpenAI, and contribute to projects involving generative models.
• UI Development and Deployment: Collaborate with UI developers to integrate ML solutions into user interfaces, ensuring seamless deployment and user experience.
• Collaboration and Communication: Work closely with cross-functional teams including data scientists, software engineers, and DevOps specialists to align MLOps initiatives with business objectives and technical requirements.
• Continuous Learning: Stay updated on the latest trends and advancements in MLOps, cloud computing, machine learning, and generative AI, and apply new knowledge to enhance our AI capabilities.

 

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Strong proficiency in Python programming.
• Hands-On knowledge of generative AI and machine learning concepts and frameworks.
• Demonstrated expertise in MLOps principles and best practices.
• Good understanding cloud platforms such as Azure, GCP, or AWS. Production level implementation and deployment for AI/ML Use Cases is a must.
• Hands-On knowledge of concepts and frameworks.