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Job Description:
We are seeking a talented and experienced GenAI MLOps Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning operations, particularly with unstructured data model building on cloud platforms such as AWS, GCP, or Azure. Additionally, a solid understanding of Kubernetes (K8S) and proficiency in Python coding are highly desirable.
Key Responsibilities:
- Design, implement, and maintain MLOps pipelines for deploying and managing machine learning models in production.
- Develop and optimize workflows for handling unstructured data, including text, images, and other non-tabular data formats.
- Collaborate with data scientists, compute/cloud engineers and other stakeholders to ensure seamless integration of machine learning models into production systems.
- Utilize cloud platforms (AWS, GCP, Azure) to build scalable and efficient machine learning solutions.
- Implement best practices for version control, continuous integration, and continuous deployment (CI/CD) of machine learning models.
- Monitor and troubleshoot production machine learning systems to ensure high availability and performance.
- Stay up-to-date with the latest advancements in MLOps, machine learning, and cloud technologies.
Required Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 4-8 years of experience in MLOps, machine learning, or a related field.
- Strong proficiency in Python programming, with experience in libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
- Experience with GenAI agent building frameworks such as AWS Bedrock, Azure Cognitive Services, Google Vertex AI, LangChain, and similar technologies.
- Proficiency in developing and deploying GenAI applications, including chatbots, retrieval-augmented generation (RAG), and other related technologies.
- Hands-on experience with cloud platforms (AWS, GCP, Azure) for deploying and managing machine learning models.
- Experience with unstructured data processing and model building.
- Knowledge of containerization and orchestration tools such as Docker and Kubernetes (K8S).
- Familiarity with CI/CD tools and practices.
- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
- Strong communication skills, both written and verbal.
Preferred Qualifications:
- Experience with natural language processing (NLP) and computer vision techniques.
- Proficiency in Databricks for managing the entire machine learning lifecycle is highly desirable.
- Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
- Familiarity with monitoring and logging tools such as Prometheus, Datadog, or Grafana.
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