What are the responsibilities and job description for the AI Platform Architect position at Maayu?
Any work authorization is finennLocation is Ashburn, VAnnExperience level is SENIORnnResponsibilities : nn
- Design and develop end-to-end applications that seamlessly integrate machine learning capabilities, including real-time inference, batch processing, and efficient data management to deliver scalable and robust solutions.n
- Identify bottlenecks in the model development, deployment, and monitoring process.n
- Design and implement production-ready machine learning pipelines, including model training, validation, deployment, and monitoring (e.g., labelled data sets to check performance of promptsn
- Build scalable, high-performance infrastructure to support Generative AI workflows (e.g., distributed training, inference optimization, and GPU / TPU utilizationn
- Deploy GenAI applications into production cloud environments with performance, cost, and latency trade-offs considered (e.g., open-source vs. closed-source, quantization, prompt token length, completion caching, prompt cachingn
- Monitor and troubleshoot model performance, addressing issues such as performance drift and response latency.n
- Stay at the forefront of Generative AI advancements, identifying opportunities to incorporate the latest research and techniques into production systems.nnQualifications : nn
- Bachelor's or advanced degree in computer science, engineering, or a related field.n
- 3 years of experience in machine learning engineering, with a focus on deploying AI systems at scale.n
- Experience working with large-scale Generative AI applications in production environments.n
- Relevant experience in the legal domain is a plus.n
- Strong proficiency in Python and machine learning frameworks (e.g., TensorFlow, PyTorchn
- Experience with Generative AI tools and techniques (e.g., LLMs, quantization, synthetic data generation, knowledge distillation, retrieval-augmented generation, fine-tuningn
- Proficiency in commons GenAI libraries (e.g., LangChain, Autogen) and cloud-native AI services (e.g., Azure search)n
- Knowledge of cloud infrastructure (e.g., Azure) and management tools for IT components, storage, networking, and caching.n
- Familiarity with ML Ops principles, including CI / CD pipelines, containerization, and automated testing for AI systems.n
- Experience with modern container platforms (e.g., Docker, OpenShift) and tools like Jenkins, Git, and Sonar.n
- Strong problem-solving skills with the ability to address complex technical challenges.n
- Excellent communication skills to collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.n
- Eagerness to stay updated with cutting-edge AI research and apply innovative ideas to real-world problems.n
- Organization and attention to detail, ensuring high-quality delivery.n
- Ability to work collaboratively to create innovative and efficient solutions. Responsibilities : Design and develop end-to-end applications with machine learning capabilities.Identify bottlenecks in model development, deployment, and monitoring processes.Implement production-ready machine learning pipelines.Build scalable infrastructure for Generative AI workflows.Deploy GenAI applications into production cloud environments.Monitor and troubleshoot model performance.Incorporate latest AI research and techniques into production systems.
