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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Software Engineer, GenAI - **Company:** Instrumentl Inc. - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $175,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Databases, Continuous Integration, Relational Databases, Python (Programming Language), Ruby on Rails, Software Engineering, SQL Databases, Google Cloud, Data Ingestion, Large Language Models, Code Testing, Build Tools, Docker - **Published:** August 15, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pb8u2hem5e ## About the Role * 7+ years of professional software engineering experience, with deep, recent, multi-year Python and strong relational database and schema design skills. * Solid CS fundamentals and a demonstrated track record of owning complex systems end to end, from design through production reliability. * At least 1 year of hands-on experience building with modern LLMs (as an IC)., * Real RAG depth: hybrid search (keyword plus vector), re-ranking or fusion methods, and grounded citations, tuned in production rather than read about. * Hands-on with at least one of LangChain, LangGraph, or LlamaIndex. * Vector databases beyond pgvector (Pinecone, Qdrant, Milvus). * Built end-to-end agentic data-processing systems (crawling, dedup, structuring) with whole-system ownership. * Evaluation and observability for AI systems: golden datasets, precision vs. recall, LLM-as-judge, and drift monitoring. * Ruby on Rails (our core platform is on Rails), deep SQL, and experience with AWS or GCP, Docker, and CI/CD. * Startup experience and comfort operating in fast, scrappy, low-process environments. ## Description You'll join our AI Engineering team as a Senior Engineer embedded in one of our product pods, reporting to our AI Engineering Lead. You'll own AI features for grant discovery end to end, from the data backbone that crawls and structures messy source data, through the RAG and agentic systems that match grants to nonprofits, to production deployment and ongoing evaluation. It's a hands-on, high-ownership seat with direct access to founders and real room to grow as the engineering team scales! What You Will Do Build agentic AI systems and ship them to production * Build tool-using LLM systems that plan, call tools, and run multi-step workflows for tasks like grant discovery, data ingestion, and research assistance. * Build agentic data-processing pipelines that crawl the web, pull and dedupe messy source data at scale, and structure it into clean, queryable databases other teams build on. * Turn prototypes into resilient production services with clear fallback, cost, and latency budgets. Own RAG and ranking end to end * Own RAG end to end: ingestion, chunking and embedding strategy, hybrid retrieval, re-ranking, citations, and grounding. * Build ranking and scoring systems that match grants to nonprofits, universities, and foundations using complex relevance techniques. * Continuously improve recall and precision and keep indices healthy as the dataset grows. Ship safely and raise the bar * Stand up evaluation and observability so our AI is grounded, safe, and cost-effective, and treat LLM behavior as non-deterministic by design rather than as a regular API. * Partner directly with founders and your pod on undefined, complex problems with real autonomy. * Write clear, maintainable, well-tested code and build reusably so your work expands across teams. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai)