AI Platform Engineer, Harness

Stelle nicht auf LinkedIn

🕒 vor 4 Tagen

🇺🇸 Vereinigte Staaten – Remote

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

🏗️ Plattformingenieur

🦅 H1B-Visum-Sponsor

Dieses Unternehmen hat in der Vergangenheit H1B-Visa gesponsert.

Jetzt Bewerben

Ähnliche Remote-Jobs finden

E-Mail-Benachrichtigung für neue Jobs einrichten

📊 Überprüfen Sie Ihre Lebenslauf-Bewertung für diese Stelle

Verbessern Sie Ihre Chancen auf ein Vorstellungsgespräch, indem Sie Ihre Lebenslauf-Bewertung vor der Bewerbung überprüfen.

Lebenslauf hochladen

LTS

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

🏥 Gesundheitswesen

💼 Beratung

📦 Logistik

Healthcare • Consulting • Logistics

LTS ist ein Unternehmen, das sich auf missionsunterstützende Dienstleistungen und Gesundheitsdienste für Behörden konzentriert und Notfallmaßnahmen, Arbeitsschutz, automatisierte Gesundheitslösungen und Unternehmensberatung anbietet. Es bietet schnelle landesweite Einsätze für Katastrophenhilfe, Logistik und Massenbetreuung und liefert automatisierte Diagnostik-Kioske, Labortests, medizinisches Personal, Vor-Ort-Kliniken sowie das Management von Tests und Impfungen für benachteiligte und abgelegene Bevölkerungsgruppen. LTS bietet außerdem IT-Modernisierung, Cloud- und Systemintegration, Cybersecurity und Risikomanagement, KI/ML und Datenanalysen sowie Programmmanagement für Bundes-, Landes- und Kommunalkunden mit starker Compliance (HIPAA und Bundesstandards) und Erfahrung im Bereich öffentlicher Ausschreibungen.

Beschreibung

• 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.

🎯 Anforderungen

• 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.

🏖️ Vorteile

• 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