Senior Agentic AI Software Engineer
New
L
LTSAI Engineering
United States - RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years of professional software engineering experience; 3+ years designing, developing, and deploying production AI applications
- Required Skills
- DockerPythonKubernetesCI/CDGenerative AILangChain
Requirements
- Bachelor's degree in Computer Science, Software Engineering, AI, or related discipline (or equivalent experience).
- 7+ years of professional software engineering experience designing and building distributed production systems.
- 3+ years designing, developing, and deploying production AI applications beyond proof-of-concept environments.
- Strong proficiency in Python and modern backend software engineering.
- Experience building enterprise APIs, microservices, and cloud-native applications.
- Hands-on experience developing applications powered by Large Language Models (LLMs) and Generative AI.
- Experience building Agentic AI solutions using frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or CrewAI.
- Strong experience designing Retrieval-Augmented Generation (RAG) architectures including embeddings, vector search, reranking, and context engineering.
- Experience with Docker, Kubernetes, Git, CI/CD pipelines, and modern DevOps practices.
- Strong understanding of software architecture, testing, observability, debugging, and production operations.
- Excellent communication skills with the ability to explain complex technical concepts.
Responsibilities
- Design, develop, and deploy autonomous and multi-agent AI systems capable of reasoning, planning, tool use, workflow automation, and human-in-the-loop collaboration.
- Build intelligent orchestration pipelines coordinating LLMs, specialized agents, enterprise tools, and structured reasoning workflows.
- Design and optimize Retrieval-Augmented Generation (RAG) pipelines including document ingestion, embeddings, hybrid retrieval, reranking, and semantic search.
- Integrate AI systems with source code repositories, enterprise documentation, APIs, and structured data.
- Design and implement scalable backend services, APIs, and cloud-native applications supporting enterprise AI workloads.
- Implement testing, evaluation, monitoring, and LLMOps practices to ensure AI systems remain trustworthy and production-ready.
- Mentor engineers through technical leadership, architecture discussions, and collaborative problem solving.
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