Senior Principal AI Solutions Engineer

Responsibilities

Build agentic and self-improving systems

• Agentic AI Architecture: Design and build single- and multi-agent systems with planning, tool and function calling, MCP, memory, and long-running workflows that are reliable enough for production
• Self-Improving Systems: Build feedback loops that make agents better over time, including automated evals, LLM-as-judge, trace-driven prompt and program optimization (e.g. DSPy-style), synthetic data generation, and fine-tuning from production signals

Evaluation and Observability

• Set up eval harnesses, tracing and guardrails so customers can measure quality, cost and latency, and trust what they deploy

Get the most from open models

• Open-Weight Model Expertise: Choose, adapt and combine open models (e.g. Llama, Qwen, DeepSeek, gpt-oss, Gemma, GLM) for customer use cases, including LoRA/PEFT fine-tuning, distillation and model routing
• Performance and Benchmarking: Benchmark end-to-end solutions, not just tokens per second, and show where fast inference changes what an application can do
• Model Integration: Validate new models and capabilities on SambaNova's platform stack and report gaps to Product and Engineering

Ship full-stack vertical solutions

• Front-End and Product Build: Build polished, usable web applications (e.g. React/Next.js, TypeScript) that non-technical knowledge workers can adopt, not just notebooks and APIs
• Vertical Knowledge Work: Build domain solutions for areas such as financial services, legal, healthcare, public sector and research: document analysis, research agents, RAG and knowledge assistants, report generation and workflow automation
• Multimodal Solutions: Build vision-language and document-understanding pipelines (OCR, charts, forms, images, video) combined with agentic reasoning
• Audio and Voice: Build real-time voice agents and audio pipelines (ASR, TTS, speech-to-speech, streaming and turn-taking) where low latency is the product

Lead with customers and shape the platform

• Solutions Technical Strategy: Set the technical direction for the solutions portfolio: which agentic, multimodal and vertical patterns we invest in, the shared frameworks and components we build, and the engineering standards they meet
• Technical Leadership and Mentorship: Act as the senior technical authority across solutions engineering; review designs, mentor engineers and lift the quality of everything the team ships
• Executive Engagement: Act as the trusted technical advisor to customer CTOs and AI leaders on architecture, build-versus-buy and scaling AI across the enterprise
• Customer Engagement: Lead technical discovery, workshops, hackathons and hands-on co-builds with strategic customers, from prototype to production
• Reference Architectures: Publish reference architectures, starter kits, open-source examples and best-practice guides that scale beyond each engagement
• SambaStack Tooling: Develop tooling and automation for SambaStack deployment, management and integration
• Product Feedback: Feed what you learn in the field (model requests, feature gaps, developer experience) into the model roadmap and platform priorities
• Thought Leadership: Represent SambaNova through demos, talks, blogs and community contributions

Requirements

Required Qualifications

• Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics or a related field
• 8+ years (IC5) or 10+ years (IC6) of industry experience in software, ML or solutions engineering, including 3+ years building LLM-based applications that reached production
• Proven experience building agentic systems: tool and function calling, multi-agent orchestration and frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK or equivalent
• Hands-on experience with open-weight models: serving, prompting, fine-tuning (LoRA/PEFT) and evaluation
• Strong full-stack skills: expert Python plus modern front-end development (TypeScript, React/Next.js), APIs and cloud deployment
• Experience designing evals and benchmarks for LLM applications, covering quality, latency and cost
• Excellent customer communication, with the ability to lead workshops and explain architecture to both executives and engineers
• A track record of technical leadership across teams: setting architecture direction, mentoring senior engineers and owning outcomes for strategic accounts

Preferred Qualifications

• Experience building self-improving or learning systems: prompt/program optimization, RL from feedback, synthetic data pipelines or continual fine-tuning
• Experience building real-time voice agents (e.g. LiveKit, Pipecat, WebRTC) and working with speech models (ASR/TTS)
• Experience with multimodal and vision-language models and document-understanding pipelines
• Domain experience in one or more knowledge-work verticals (financial services, legal, healthcare, public sector, scientific research)
• Familiarity with inference frameworks (vLLM, SGLang, TensorRT-LLM) and hardware-aware performance tuning
• Experience with MCP, RAG at enterprise scale, and agent security and guardrails
• Open-source contributions, public demos or technical content in the AI community