{"schemaVersion":"jobsearcher.job.v1","id":"55eb66e119af15ef1b9f37bd","url":"https://jobsearcher.com/jobs/55eb66e119af15ef1b9f37bd","canonicalUrl":"https://jobsearcher.com/jobs/55eb66e119af15ef1b9f37bd","title":"AI/ML Engineer — LLM & Agent Stack","description":"Role summaryYou’ll be an individual contributor embedding modern LLM applications into customer workloads and platform features. You’ll work closely with senior engineers and products to implement production-grade RAG pipelines, prompt/chain design, small agent runtimes (LangGraph/LangChain), vector DB integrations, and monitoring instrumentation on TrueFoundry. This role is hands-on and ideal if you enjoy shipping code, learning the latest agent tools, and turning prototypes into repeatable engineering patterns.What you’ll doImplement, test, and maintain LLM-powered features and AI Agent / RAG pipelines (prompting, retrieval, vector DB + embeddings).Build and extend agent workflows using LangGraph / LangChain or equivalent frameworks; help harden state persistence and retry logic.Integrate models and runtimes via the platform’s API (deploy/serve/instrument LLMs, configure token/cost guards).Write end-to-end tests, small services, and automation to reproduce customer issues and demo solutions.Instrument observability: logs, traces, latency/cost dashboards and basic alerting for LLM workloads.Collaborate with product, support, and customers to convert POCs into documented, repeatable patterns.Must-have2–3 years software engineering experience building backend services or ML infra; comfortable with Python (and one other language).Practical experience using LLMs (OpenAI/Anthropic/other) and building prompt + retrieval workflows.Familiarity with at least one vector DB (e.g., Chroma, Pinecone, Weaviate) and embeddings pipelines.Experience with REST/gRPC APIs, containers (Docker), and basic Kubernetes concepts.Strong debugging skills and ability to write clean, testable code.Nice-to-haveHands-on with LangChain or LangGraph and agent architectures.Experience with RAG evaluation, prompt engineering best practices, or prompt-testing frameworks.Exposure to production monitoring for LLMs (token usage, cost controls, latency SLAs).Prior experience deploying to or operating on Kubernetes.Qualifications & signals we likeBS/MS in CS or related field (or equivalent industry experience).Public repo or demo showing an LLM project, small agent, or RAG pipeline.Curiosity about LLM safety, reliability, and cost-efficient deployment.","company":"Truefoundry","rawCompany":"truefoundry","city":"San Mateo","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-04-12T17:02:28.750Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI/ML Engineer — LLM & Agent Stack","description":"Role summaryYou’ll be an individual contributor embedding modern LLM applications into customer workloads and platform features. You’ll work closely with senior engineers and products to implement production-grade RAG pipelines, prompt/chain design, small agent runtimes (LangGraph/LangChain), vector DB integrations, and monitoring instrumentation on TrueFoundry. This role is hands-on and ideal if you enjoy shipping code, learning the latest agent tools, and turning prototypes into repeatable engineering patterns.What you’ll doImplement, test, and maintain LLM-powered features and AI Agent / RAG pipelines (prompting, retrieval, vector DB + embeddings).Build and extend agent workflows using LangGraph / LangChain or equivalent frameworks; help harden state persistence and retry logic.Integrate models and runtimes via the platform’s API (deploy/serve/instrument LLMs, configure token/cost guards).Write end-to-end tests, small services, and automation to reproduce customer issues and demo solutions.Instrument observability: logs, traces, latency/cost dashboards and basic alerting for LLM workloads.Collaborate with product, support, and customers to convert POCs into documented, repeatable patterns.Must-have2–3 years software engineering experience building backend services or ML infra; comfortable with Python (and one other language).Practical experience using LLMs (OpenAI/Anthropic/other) and building prompt + retrieval workflows.Familiarity with at least one vector DB (e.g., Chroma, Pinecone, Weaviate) and embeddings pipelines.Experience with REST/gRPC APIs, containers (Docker), and basic Kubernetes concepts.Strong debugging skills and ability to write clean, testable code.Nice-to-haveHands-on with LangChain or LangGraph and agent architectures.Experience with RAG evaluation, prompt engineering best practices, or prompt-testing frameworks.Exposure to production monitoring for LLMs (token usage, cost controls, latency SLAs).Prior experience deploying to or operating on Kubernetes.Qualifications & signals we likeBS/MS in CS or related field (or equivalent industry experience).Public repo or demo showing an LLM project, small agent, or RAG pipeline.Curiosity about LLM safety, reliability, and cost-efficient deployment.","datePosted":"2026-04-12T17:02:28.750Z","dateModified":"2026-04-12T17:02:28.750Z","hiringOrganization":{"@type":"Organization","name":"Truefoundry","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Mateo","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"55eb66e119af15ef1b9f37bd"},"url":"https://jobsearcher.com/jobs/55eb66e119af15ef1b9f37bd"}}