LTS - Senior Agentic AI Software Engineer
Requirements
• 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. • 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.
Responsibilities
• Build Intelligent Agentic Systems • 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. • 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 • 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.
Benefits
• The Opportunity to support high-visibility federal missions • A culture that values innovation, growth, and collaboration • Access to cutting-edge tools and technologies • Comprehensive benefits for you and your family • A career path that rewards ambition and performance • If you’re ready to push boundaries, sharpen your skills, and join a team that is passionate about building what’s next, we’d love to meet you. Apply today and let’s build a future together!
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