Senior Agentic AI Software Engineer

LTS

Senior Agentic AI Software Engineer developing autonomous AI systems and orchestration pipelines for LTS. Building intelligent solutions for modernizing legacy software systems in federal healthcare environments.

Posted 7/30/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying autonomous AI systems, with a strong focus on Retrieval-Augmented Generation (RAG) architectures and integration of AI with enterprise tools. Proven ability to lead technical discussions, mentor engineers, and establish best practices in AI engineering.

Highest-signal resume keywords

Python ProficiencyDistributed Systems DesignRetrieval-Augmented Generation (RAG)Large Language Models (LLMs)Cloud-Native Application Development

ATS Keywords

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Hard Skills

Software EngineeringAI Application DevelopmentMicroservices ArchitectureAPI DevelopmentPerformance OptimizationTesting and ObservabilitySecurity and ReliabilityModel ExplainabilitySystem ArchitectureProblem Solving

Soft Skills

Excellent CommunicationTechnical LeadershipCollaborative Problem Solving

Tools & Technologies

DockerKubernetesGitCI/CD PipelinesLangGraphLangChainLlamaIndexSemantic KernelAutoGenCrewAI

Industry Keywords

AI SystemsEnterprise APIsCloud PlatformsBusiness ApplicationsAutonomous AgentsModel-Driven Engineering

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerKubernetesMicroservicesPython

About the role

Key responsibilities & impact

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.
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.
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.
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.
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Comprehensive benefits for you and your family
Access to cutting-edge tools and technologies
A culture that values innovation, growth, and collaboration
The Opportunity to support high-visibility federal missions
A career path that rewards ambition and performance