ML Engineer I

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.