Associate Principal Engineer, Machine Learning
Tasks
- Apply statistical techniques to real-world problems
- Build deep learning models
- Build multi-agent AI systems
- Conduct root cause analysis
- Define benchmarks for non functional requirements
- Deploy machine learning pipelines
- Design AI/ML architecture
- Design deployment with Docker and Kubernetes
- Design prompt templates for LLMs
- Develop NLP solutions
- Develop computer vision solutions
- Develop data-driven models
- Develop generative AI solutions
- Implement MLOps practices
- Implement retrieval-augmented generation
- Orchestrate AI agents
- Review architecture for scalability and security
- Run proof of concepts for proposed technologies
- Write and review design documents
Perks/Benefits
- N/A
Skills/Tech-stack
Adversarial Networks | Agent Orchestration | Agent systems | Computer Vision | Deep learning | Deployment Pipelines | Docker | Generative Adversarial Networks | JAX | Kubeflow | Kubernetes | LLM | Language Processing | MLOps | MLflow | Machine Learning | Model Deployment | Model deployment pipelines | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | NumPy | Pandas | Prompt engineering | Prompt templates | PyTorch | Python | R | Responsible AI | Retrieval-Augmented Generation | SQL | Scikit-learn | Statistical modeling | TensorFlow | Variational Autoencoders
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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