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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic AI Software Engineer - **Company:** LTS, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $113,000.0 - $206,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Business Software, Cloud Computing, Databases, Continuous Integration, Software Debugging, DevOps, Distributed Systems, Graph Database, Python (Programming Language), Microsoft Office, Open Source Technology, Software Architecture, Search Technologies, Software Engineering, Systems Architecture, Systems Integration, Model-Driven Development, Spring Cloud, Genesys, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Software Application Programming, Generative AI, Backend, Git, Kubernetes, Information Technology, Low Latency, Machine Learning Operations, Virtual Agents, Mixed Reality, Docker, Microservices - **Published:** July 31, 2026 - **Apply:** https://job-boards.greenhouse.io/lts/jobs/4340374009 ## About the Role * Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Engineering, or a related technical discipline (or equivalent professional experience). * 7+ years of professional software engineering experience designing and building distributed production systems. * At least 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, CrewAI, or comparable technologies. * Strong experience designing Retrieval-Augmented Generation (RAG) architectures including embeddings, vector search, hybrid retrieval, reranking, context engineering, and grounding techniques. * Experience integrating AI systems with enterprise APIs, databases, cloud platforms, and business applications. * 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 to both engineering and business stakeholders. * Ability to solve difficult engineering problems from first principles. * Ability to think deeply about system architecture, reliability, and scalability. * Passionate about explainability as model performance. * Ability to move comfortably between distributed systems, AI frameworks, and product engineering. * Willingness to take ownership of ambiguous, high-impact technical challenges. * Background with using AI coding assistants, autonomous agents, and model-driven engineering workflows. * A technically skilled engineer with a preference for building products that create lasting impact over incremental feature development. Nice to Have: * Experience developing multi-agent AI systems and collaborative agent workflows. * Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Vertex AI, AWS Bedrock, or open-source LLMs. * Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search. * Experience implementing LLMOps or MLOps practices. * Familiarity with graph databases, knowledge graphs, or dependency analysis. * Experience working with software engineering tools, code intelligence platforms, or developer productivity products. * Experience building AI systems in healthcare, Federal Government, or other highly regulated environments. * Familiarity with Responsible AI, AI governance, privacy, security, and compliance best practices. * Experience using AI coding assistants and autonomous agents as part of daily software development. ## Description * 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. * Develop reusable agent architectures and orchestration patterns that accelerate intelligent application development across the platform. Engineer Enterprise Retrieval & Knowledge Systems * Design and optimize Retrieval-Augmented Generation (RAG) pipelines including document ingestion, embeddings, hybrid retrieval, reranking, semantic search, context engineering, and prompt orchestration. * Integrate AI systems with source code repositories, enterprise documentation, APIs, structured data, and knowledge repositories. * Ensure every AI-generated response is explainable, evidence-based, and traceable to authoritative sources. Build Production Software * Design and implement scalable backend services, APIs, and cloud-native applications supporting enterprise AI workloads. * Develop distributed systems capable of serving low-latency AI experiences while maintaining security, reliability, and observability. * Optimize performance, latency, throughput, model quality, and infrastructure cost across production AI systems. Deliver Reliable AI * Implement testing, evaluation, monitoring, observability, guardrails, and LLMOps practices to ensure AI systems remain trustworthy and production-ready. * Continuously evaluate emerging models, frameworks, and engineering practices to improve platform capabilities. * Build AI systems that behave predictably in highly regulated enterprise environments. Collaborate Across the Product Team * Partner closely with AI architects, platform engineers, front-end engineers, designers, and product leaders to deliver cohesive AI-powered experiences. * Mentor engineers through technical leadership, architecture discussions, design reviews, and collaborative problem solving. * Help establish engineering standards, reusable frameworks, and best practices across the AI engineering organization., Lead agile teams to design, build, and operate enterprise-grade software and agentic workflows that act on production systems. Mentor others, produce designs, enforce guardrails (blast-radius, rollback, CI gates), integrate LLM APIs and agentic frameworks, and prioritize security, vulnerability management, observability, and quality across deployments. Top Skills: Agentic FrameworksApple Platforms (MacosBambooCiClaude CodeiOSIpadosJenkinsLlm ApisTvos) Genesys Software Engineer 6 Days Ago Remote 2 Locations 205K-361K Annually Senior level 205K-361K Annually Senior level Artificial Intelligence * Big Data * Cloud * Machine Learning * Software Lead design, build, and scaling of Agentic AI and industry-specific solution accelerators on Genesys Cloud. Develop reusable multi-agent workflows, RAG-based knowledge orchestration, integrations with enterprise systems, prototypes, and production-grade AI applications. Provide technical leadership, mentor engineers, collaborate with product and industry SMEs, and deliver repeatable solution blueprints to accelerate deployment and time-to-value. Top Skills: Ai SkillsAi StudioAnthropic ApisAPIsArchitectAutogenAWSAws BedrockAzureCcaasCopilotCrewaiCRMEnterprise SearchErpEvent FrameworksEvent-Driven ArchitecturesExperience OrchestrationGenesys CloudGCPJavaScriptJourney ManagementLangchainLanggraphLlm IntegrationMicroservicesMulti-Agent SystemsOpenai ApisPythonRag (Retrieval-Augmented Generation)Rest ApisSemantic KernelTypescriptVector Databases NVIDIA ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Beyond Chatbots: How to build Agentic AI systems](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems) - [AI Killed DevOps... 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