MLOps and Generative AI Engineer Consultant
2 weeks ago
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:
1. MLOps Pipeline Development: Design, implement, and maintain robust MLOps pipelines to facilitate seamless model training, evaluation, deployment, and monitoring.
2. Cloud Infrastructure Management: Utilize your expertise in Azure, GCP, or AWS to manage and optimize cloud infrastructure, ensuring efficient and scalable machine learning workflows.
3. Python Development: Leverage your python skills to develop and optimize code for machine learning workflows, data processing, and automation scripts.
4. Machine Learning Support: Provide support for machine learning tasks, including data pre-processing, feature engineering, model training, and evaluation.
5. Generative AI Understanding: Understanding of generative AI concepts and frameworks such as Langchain, LLamaIndex, or OpenAI, and contribute to projects involving generative models.
6. UI Development and Deployment: Collaborate with UI developers to integrate ML solutions into user interfaces, ensuring seamless deployment and user experience.
7. 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.
8. 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:
1. Bachelor’s degree in Computer Science, Engineering, or a related field.
2. Strong proficiency in Python programming.
3. Hands-On knowledge of generative AI and machine learning concepts and frameworks.
4. Demonstrated expertise in MLOps principles and best practices.
5. Good understanding cloud platforms such as Azure, GCP, or AWS. Production level implementation and deployment for AI/ML Use Cases is a must.
6. Hands-On knowledge of concepts and frameworks.
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