AI Platform Engineer, Harness

🕒 July 29

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🏗️ Platform Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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LTS

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

LTS is a government-focused mission support and healthcare services firm that provides emergency response, occupational health, automated healthcare solutions, and enterprise consulting. It offers rapid nationwide deployment for disaster response, logistics and mass care, and delivers automated diagnostic kiosks, diagnostic lab testing, medical staffing, on-site clinics, and testing/vaccination management for underserved and remote populations. LTS also provides IT modernization, cloud and systems integration, cybersecurity and risk management, AI/ML and data analytics, and program management for federal, state, and local clients with strong compliance (HIPAA and federal standards) and public-sector acquisition experience.

📋 Description

• Design, build, and maintain enterprise AI platform capabilities supporting Large Language Models (LLMs), AI agents, RAG, and Generative AI applications. • Develop reusable AI harnesses to automate testing, prompt evaluation, model benchmarking, regression testing, and quality assurance. • Build AI evaluation frameworks to measure model accuracy, retrieval quality, hallucination detection, latency, throughput, cost, and overall application performance. • Implement observability and monitoring solutions for AI applications, including telemetry, tracing, logging, dashboards, and operational metrics. • Build and maintain LLMOps pipelines supporting model deployment, versioning, evaluation, experimentation, rollback, and continuous improvement. • Design automated workflows for prompt testing, retrieval evaluation, AI system validation, and performance benchmarking. • Develop internal tools for prompt management, model experimentation, AI performance optimization, and developer productivity. • Build scalable backend services and APIs supporting AI platforms and enterprise AI integrations. • Collaborate with AI architects and engineering teams to integrate LLMs, RAG pipelines, vector databases, and agentic AI solutions into enterprise applications. • Support deployment of AI services across AWS, Azure, or Google Cloud using containerized and cloud-native architectures. • Implement CI/CD pipelines and infrastructure automation supporting enterprise AI development and deployment. • Apply security, governance, and Responsible AI controls throughout the AI development lifecycle. • Evaluate emerging AI frameworks, LLMOps technologies, evaluation methodologies, and automation tools to improve engineering productivity. • Troubleshoot production AI issues and continuously improve platform reliability, scalability, security, and user experience. • Document engineering standards, AI platform architecture, evaluation methodologies, and operational best practices.

🎯 Requirements

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field. • 5+ years of experience in software engineering, platform engineering, backend engineering, DevOps, cloud engineering, or infrastructure engineering. • 2+ years building or supporting Generative AI, Large Language Model (LLM), or machine learning applications. • Strong programming experience in Python. • Experience developing APIs, backend services, and distributed systems. • Experience with cloud platforms including AWS, Azure, or Google Cloud Platform. • Experience deploying applications using Docker and Kubernetes. • Experience working with Git, CI/CD pipelines, Infrastructure as Code (IaC), and infrastructure automation. • Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt engineering, Embeddings, Vector databases, AI agents and agentic workflows • Familiarity with AI evaluation techniques, automated testing, benchmarking, regression testing, and model validation. • Experience building scalable, production-grade software platforms. • Strong problem-solving, debugging, and performance optimization skills.

🏖️ 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