Senior AI Engineer – Agentic Systems & LLM Applications
New
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JobgetherAI Engineering
Primary hiring regions include the United States, Europe, and India.ContractSenior
Salary not disclosed
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Job Details
- Required Skills
- Node.jsPythonTypeScriptLLMLangChain
Requirements
- Proven experience building production applications using large language models, AI agents, or other advanced AI technologies, ideally within an AI-native startup, research environment, or developer-tool organization.
- Deep understanding of agentic systems, including agent frameworks, tool calling/function calling, autonomous planning, and reasoning loops.
- Expert-level proficiency in Python and/or TypeScript with Node.js.
- Hands-on experience with LangChain, LlamaIndex, or custom-built AI orchestration frameworks.
- Strong expertise with vector databases such as Pinecone, Weaviate, or Milvus.
- Experience designing efficient machine learning and data pipelines.
- Strong understanding of foundation model applications and the practical challenges of deploying LLM-powered systems at scale.
- Experience building sophisticated RAG architectures and retrieval strategies.
- Strong product intuition and the ability to translate emerging AI capabilities into useful, reliable products rather than purely academic experiments.
- Experience with production AI engineering practices, including evaluation, monitoring, optimization, and reliability.
- Ability to work effectively in a distributed, global-first environment with a high degree of autonomy and ownership.
- Strong problem-solving skills and a willingness to work on complex, ambiguous technical challenges.
Responsibilities
- Design and implement autonomous agents and multi-agent orchestration systems capable of handling complex, open-ended tasks.
- Build and optimize production-grade applications on top of leading foundation models, with a focus on reliability, observability, scalability, and low-latency performance.
- Architect advanced Retrieval-Augmented Generation (RAG) pipelines and vector database strategies that provide agents with accurate, relevant, and real-time context.
- Develop sophisticated tool-calling, planning, reasoning, and autonomous execution workflows for AI agents.
- Contribute to AI-powered developer tools and internal frameworks that accelerate the development and deployment of AI-native products.
- Establish rigorous evaluation frameworks and Evals to measure model and agent performance in production environments.
- Implement monitoring, fine-tuning, testing, and optimization pipelines to improve accuracy, reliability, and safety.
- Collaborate across product and engineering functions to integrate foundation models into solutions that address real-world user needs.
- Help shape the technical architecture and engineering practices for scalable AI-native systems.
- Continuously evaluate emerging AI technologies, frameworks, and approaches and identify opportunities to incorporate them into production systems.
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