AI Principal Machine Learning Engineer
New
J
JobgetherNetwork intelligence
Based in the United StatesFull-TimePrincipal
Salary170,000 - 240,000 USD per year
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Job Details
- Experience
- Between 5 and 10 years of experience across the full software development lifecycle; at least 3 years of experience leading the design and architecture of large-scale distributed systems.
- Required Skills
- DockerPythonKubernetesMachine LearningFastAPICI/CDRESTful APIsMicroservicesGenerative AIDistributed Systems
Requirements
- Hold a degree in Mathematics, Computer Science, or a related technical discipline.
- Have 5–10 years of experience across the full software development lifecycle, including system design, coding, code reviews, testing, build processes, deployments, and operations.
- Have 5–10 years of hands-on Python experience, including advanced language features, libraries, and development practices.
- Bring expertise in RESTful API design and practical experience with frameworks such as FastAPI.
- Be proficient in Docker, Kubernetes, and modern CI/CD practices.
- Have at least 3 years of experience leading the design and architecture of large-scale distributed systems, preferably in cloud environments.
- Have experience as a technical mentor, technical lead, or engineering team leader.
- Understand software architecture, scalable microservices, and high-performance computing.
- Preferred: Master's or PhD in Computer Science, or equivalent advanced experience in machine learning.
- Preferred: Practical experience with PyTorch, Amazon SageMaker, NVIDIA Triton, and TensorRT.
- Preferred: Experience with NoSQL and document-oriented databases.
- Preferred: Experience managing large-scale data and optimizing processing workflows.
Responsibilities
- Shape machine learning strategies and product solutions across multiple platforms.
- Design, develop, and launch machine learning and generative AI capabilities.
- Lead architecture, design, coding, testing, deployment, and ongoing operations across the software development lifecycle.
- Drive technical discussions and participate in design reviews, code reviews, and implementation.
- Architect and build high-performance, scalable microservices, including synchronous and asynchronous web services.
- Develop real-time online inference solutions using technologies such as NVIDIA Triton, TensorRT, and mixed-precision computing.
- Contribute to initiatives involving intelligent agents, RAG, knowledge bases, data mining, anomaly detection, and LLM fine-tuning.
- Build resilient distributed systems and cloud-based architectures.
- Mentor engineers and strengthen technical capabilities.
- Improve engineering processes, system performance, reliability, scalability, and operational practices.
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