Develop and maintain LLM pipelines, design RAG architectures, implement agentic systems, and integrate AI features into data pipelines.
Draup is a Series A-funded agentic AI company building the intelligence layer for how global enterprises make workforce and go-to-market decisions. We work with 250+ enterprise clients — including 5 of the Fortune 10 — processing 1B+ job descriptions, 850M+ professional profiles, and signals from 100+ labor databases.
We are now building our Silicon Valley engineering team — a small, senior group focused on next-generation AI research and product.
Location: San Francisco, SoMa — minimum 4 days per week in-office.
What you'll do
• Build and maintain production-grade LLM pipelines and agentic workflows.
• Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale.
• Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent coordination, and reasoning loops.
• Own prompt engineering, model versioning, evaluation (RAGAS, DeepEval), and LLMOps instrumentation.
• Integrate AI features into large-scale data pipelines; maintain observability and guardrails in production.
What we require
• BS/MS in Computer Science, Machine Learning, or related field.
• 3–5 years of AI/ML engineering; minimum 2 years building LLM-powered systems shipped to production.
• Strong Python; PyTorch or Hugging Face Transformers; AWS or GCP; Docker/Kubernetes.
• Portfolio of shipped AI work required — agentic pipelines, RAG systems, or fine-tuned models.
• No visa sponsorship. Must be authorized to work in the US without current or future employer sponsorship.
Similar Jobs
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.
Top Skills:
Agentic SystemsSparkAWSDatabricksDeep LearningEmbeddingsInformation RetrievalJavaKotlinLarge Language ModelsNlpPythonRagRankingRecommendationsSQLTypescript
Artificial Intelligence • Cloud • Software • Infrastructure as a Service (IaaS)
Lead DigitalOcean’s AI security strategy, governance framework, roadmap, and organizational programs. Build and operationalize AI/ML security controls, tooling, guardrails, and automation; conduct threat modeling, adversarial testing, and AI red teaming. Review AI architectures, guide high-risk product decisions, monitor deployed models, and address risks involving RAG, agents, model supply chains, and prompt injection. Represent AI security to executives and external communities while translating emerging research and threats into engineering guidance and policy.
Top Skills:
Agentic Ai FrameworksAi/MlEu Ai ActGoIso/Iec 42001Mitre AtlasModel ApisNist Ai RmfOwasp Llm Top 10PythonRag
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.
Top Skills:
3D ParallelismCompiler ToolchainsDdpDeepspeedFsdpMegatron-LmPythonPyTorchQuantizationWsd
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
