Principal Software Architect

🕒 il y a 2 mois

🍂 Massachusetts – Distant

🍂 Massachusetts – Remote

💵 $168 000 - $210 000 / an

⏰ Temps Plein

🔴 Expert

🧑‍💻 Développeur Full-Stack

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

👻 Score fantôme 5%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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eClinical Solutions

Site WebLinkedInTous les Emplois

201 - 500 employés

Fondée en 2012

💼 Conseil

🏥 Santé

🧬 Biotechnologie

💰 Private Equity Round en 2020-01

Consulting • Healthcare • Biotechnology

eClinical Solutions est une entreprise spécialisée dans la gestion des données cliniques, offrant des solutions intégrées de révision et de gestion de données pour les industries biotechnologique et pharmaceutique. Leur plateforme, elluminate, permet aux organisations de moderniser et automatiser leurs flux de travail de données cliniques, garantissant une acquisition, une gestion et une analyse des données efficaces. En s’appuyant sur des analyses avancées et des approches technologiques, eClinical Solutions aide à rationaliser les opérations d'essais cliniques et à améliorer la productivité, permettant ainsi une meilleure prise de décision dans la recherche clinique.

Description

• Lead the design and evolution of AI-enabled software platforms • Translate business and product needs into scalable, secure, and responsible technical solutions • Partner with software engineers, product teams, data specialists, and business stakeholders • Identify opportunities for AI and guide architecture decisions • Review implementations and mentor teams on modern engineering practices, AI-assisted development, and sound design principles • Research emerging AI, machine learning, generative AI, cloud, and software architecture technologies and evaluate product-platform fit • Analyze features, data flows, and system designs for scalability, performance, security, and AI-readiness • Document current and future architectural patterns, AI integration patterns, model lifecycle considerations, data governance, observability, and responsible AI guardrails • Define reference architectures for data ingestion, retrieval-augmented generation, model integration, inference services, monitoring, and human-in-the-loop workflows • Promote responsible AI practices including privacy, security, explainability, bias mitigation, regulatory awareness, and appropriate enterprise-data use • Guide teams in using AI-assisted engineering tools to improve productivity, code quality, documentation, testing, and delivery velocity • Integrate LLMs and AI services into .NET- and Python-based systems • Design AI-assisted workflows, copilots, and intelligent automation features • Apply prompt engineering, evaluation techniques, and guardrails for reliability and compliance • Collaborate with data and platform teams to operationalize AI in production

🎯 Exigences

• 10+ years in web application development, service-oriented architecture, cloud-native platforms, and AI-enabled application design preferred • 10+ years in full-stack enterprise application development, with experience integrating AI, automation, analytics, or data-driven capabilities preferred • 10+ years leading Software Engineering teams, including mentoring architects and engineers on AI adoption, architectural trade-offs, and modern delivery practices preferred • Ability to evaluate AI platforms and frameworks and present comparative analysis of benefits, risks, costs, and implementation considerations • Strong problem-solving abilities • Excellent written and verbal communication skills • Ability to influence technical strategy across product, engineering, security, data, and business stakeholders while advocating for responsible and practical AI adoption • Mastery of software architecture and design, with strong understanding of AI-enabled system design patterns • Deep understanding of Microsoft .NET and modern application integration patterns for AI-enabled services • Expert level in relational and non-relational database design, data modeling, and data architecture for analytics and AI use cases • Experience with enterprise applications in a SaaS cloud environment, including AWS, Azure, and scalable deployment patterns for AI, ML, and data-intensive workloads • Knowledge of AWS products and deployment, cloud AI services, model hosting, automation, monitoring, and secure integration patterns • Familiarity with AI/ML concepts including model lifecycle management, prompt engineering, retrieval-augmented generation, evaluation frameworks, observability, and MLOps practices • Understanding of responsible AI, enterprise data protection, privacy, security, compliance, and governance considerations for production AI systems

🏖️ Avantages

• Remote work recognition and work-life balance • Inclusive, people-first culture • Opportunities to learn, grow, and continuously improve • Equal opportunity employment

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