Senior Applied AI Engineer

LTS

1d•Remote

About The Position

LTS is seeking a highly skilled Senior Applied AI Engineer to focus on continuously improving the intelligence behind the platform. You'll experiment with models, optimize retrieval strategies, refine agent reasoning, evaluate AI performance, and transform emerging AI capabilities into production-ready solutions. The Agentic AI platform is designed to help engineers understand, analyze, and modernize one of the most consequential legacy software systems still operating today. Our platform enables engineers to ask questions in plain English and receive explainable, verifiable answers traced directly back to decades of production source code. Rather than replacing engineers, we're building AI that accelerates engineering through transparency, traceability, and intelligent reasoning. We're building an AI-native engineering platform supporting the modernization of mission-critical healthcare systems serving millions of Veterans nationwide. The platform is designed for deployment across federal enterprise environments and is being engineered to align with FedRAMP security controls, Zero Trust principles, and federal compliance requirements. The platform has executive sponsorship, committed users, and a customer investing in long-term modernization. Our engineering team is intentionally small giving every engineer meaningful ownership, and direct influence over product direction. We don't simply build AI-powered software—we build software with AI. This is not another chatbot. Using LLMs, autonomous agents, AI-assisted development, parallel workflows, and model-driven engineering is simply how we work.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional experience).
5+ years of software engineering, applied AI, machine learning, or AI systems development experience.
Demonstrated experience developing production AI applications powered by Large Language Models (LLMs).
Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems.
Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval.
Experience evaluating AI model performance and implementing experimentation frameworks.
Strong programming skills in Python and experience with modern software engineering practices.
Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
Experience with using AI coding assistants as part of your daily workflow.
Familiarity with REST APIs, cloud-native applications, and distributed software systems.
Strong analytical, problem-solving, and communication skills.
Intellect and curiosity for AI systems and how they behave.
Deep passion for experimenting with new AI techniques.
Background in evaluation, explainability, and continuous improvement.
Proven success with ownership of difficult technical challenges and collaboration across disciplines.

Nice To Haves

Experience optimizing autonomous or multi-agent AI systems.
Experience implementing automated AI evaluation frameworks.
Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Vertex AI, AWS Bedrock, or open-source LLMs.
Experience with vector databases including Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search.
Experience with Responsible AI, AI governance, safety, and explainability.
Familiarity with software engineering tools, code intelligence platforms, or developer productivity solutions.
Experience supporting healthcare, Federal Government, or other highly regulated environments.
Experience using AI coding assistants and autonomous agents as part of daily software development.

Responsibilities

Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity.
Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement.
Rapidly prototype new AI capabilities and transition successful experiments into production.
Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management.
Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques.
Improve AI response quality through experimentation, benchmarking, and iterative optimization.
Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness.
Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems.
Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement.
Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management.
Design approaches that maximize retrieval quality across large technical documentation and source code repositories.
Improve how AI agents discover, organize, and reason over enterprise knowledge.
Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform.
Share research findings, experimental results, and engineering recommendations with cross-functional teams.
Help establish best practices for experimentation, evaluation, and AI quality throughout the organization.