AI Platform Engineer, Harness

Emploi pas sur LinkedIn

🕒 il y a 8 jours

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟡 Intermédiaire

🟠 Senior

🏗️ Ingénieur Plateforme

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

Postuler Maintenant

Trouver des Emplois à Distance Similaires

Configurer une alerte email pour les nouveaux emplois

📊 Vérifiez votre score de CV pour ce poste

Améliorez vos chances d'obtenir un entretien en vérifiant votre score de CV avant de postuler.

Télécharger le CV

LTS

Site WebLinkedInTous les Emplois

1001 - 5000 employés

🏥 Santé

💼 Conseil

📦 Logistique

Healthcare • Consulting • Logistics

LTS est une entreprise axée sur la mission de soutien gouvernemental et les services de santé, fournissant des réponses d'urgence, de la santé au travail, des solutions de santé automatisées et du conseil en entreprise. Elle offre un déploiement rapide à l'échelle nationale pour les interventions en cas de catastrophe, la logistique et les soins massifs, et met à disposition des kiosques de diagnostic automatisés, des tests de laboratoire diagnostiques, du personnel médical, des cliniques sur site ainsi que la gestion des tests/vaccinations pour les populations mal desservies et éloignées. LTS propose également la modernisation des systèmes informatiques, l'intégration et la gestion des systèmes dans le cloud, la cybersécurité et la gestion des risques, l'IA/ML et l'analyse des données, et la gestion de programme pour les clients fédéraux, étatiques et locaux avec une forte conformité (HIPAA et normes fédérales) et une expérience d'acquisition dans le secteur public.

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.

🎯 Exigences

• 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.

🏖️ Avantages

• 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