Marvell is looking to solve the problem of developing and deploying Agentic AI systems that can take meaningful actions, orchestrate workflows, and assist with complex tasks, bridging the gap between theoretical research and practical, real-world deployment.
Requirements
- Proficiency in programming languages such as Python, R, or Java.
- Solid understanding of machine learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch).
- Prior exposure to data analysis, statistical modeling, and experimental design.
- Knowledge of software development best practices, including version control (e.g., Git).
- Previous experience with AI/ML projects, especially agent-based AI development.
- Familiarity with advanced topics such as deep learning, reinforcement learning, or natural language processing.
- Exposure to agent orchestration frameworks (e.g., LangChain, CrewAI, Semantic Kernel).
Responsibilities
- Lead the design and development of Agentic AI applications and workflows for real-world use cases.
- Conduct research and experiments on new AI agent architectures and orchestration strategies.
- Implement and optimize machine learning models and agent pipelines for performance and scalability.
- Develop prototypes and proof-of-concept AI agents capable of performing multi-step tasks.
- Benchmark and analyze the performance of existing AI agents, proposing enhancements to improve reliability and effectiveness.
- Work with data engineers and software developers to integrate agents into larger platforms and applications.
- Support the development of secure and compliant agent-based workflows that handle sensitive data.
Other
- Currently pursuing a degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
- Strong problem-solving skills and ability to work in a collaborative team environment.
- Familiarity with cloud computing services (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
- Understanding of DevOps principles and experience with CI/CD pipelines.
- Experience optimizing machine learning models for deployment and scalability.
