Key skills
PythonC++CAIMachine LearningMLDeep LearningGenerative AILLMLangChainAgenticPyTorchTensorFlowJAXLangGraphPerformance OptimizationLeadershipCommunicationPresentation Skills
About this role
SambaNova is a leading company in the AI sector, offering a generative AI platform optimized for enterprise and government organizations. The Principal AI Solutions Engineer will drive product adoption through advanced AI solutions development and customer-facing architecture leadership.
Responsibilities:
- AI Solutions Development: Develop robust, efficient, and scalable AI solutions for enterprise customers
- Agentic AI Implementation: Lead the design and implementation of agentic AI systems and multi-agent architectures
- Performance Benchmarking: Conduct comprehensive performance benchmarking and optimization of AI models and systems
- Customer Engagement: Lead technical enablement for CDPP customers through workshops and hands-on implementation sessions
- Model Integration: Integrate latest model architectures into SambaNova's platform stack
- End-to-End Solutions: Create complete ML application solutions from concept to production deployment
- Inference Optimization: Enable high-throughput, low-latency inference deployments for customer workloads
- Cross-Functional Leadership: Collaborate with product and engineering teams to drive platform improvements
- Technical Thought Leadership: Contribute to reference architectures, best practices, and technical documentation
Requirements:
- Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or Statistics
- 5+ years of industry experience in software engineering or ML engineering
- 3+ years of hands-on experience with deep learning and machine learning systems
- Proficiency in Python or C++ programming
- Experience with modern ML frameworks: PyTorch, TensorFlow, or JAX
- Real-world experience deploying LLM-based applications
- Strong client communication and presentation skills
- Experience with benchmarking methodologies and performance optimization
- Experience with multi-modal AI systems (vision, audio, text)
- Hands-on experience with distributed training/inference frameworks: vLLM, DeepSpeed, Megatron-LM
- Experience building agentic AI systems (LangChain, LangGraph, CrewAI, etc.)
- Knowledge of enterprise AI deployment patterns
- Experience with technical partnerships and customer-facing roles