Principal Software Architect

🔥 0 minutes ago

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $168k - $210k / year

⏰ Full Time

🔴 Lead

🧑‍💻 Full-stack Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 0%

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eClinical Solutions

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2012

💼 Consulting

🏥 Healthcare

🧬 Biotechnology

💰 Private Equity Round on 2020-01

Consulting • Healthcare • Biotechnology

eClinical Solutions is a clinical data management company that provides integrated data review and management solutions for the biotech and pharmaceutical industries. Their platform, elluminate, empowers organizations to modernize and automate their clinical data workflows, ensuring efficient data acquisition, management, and analysis. By leveraging advanced analytics and technology-driven approaches, eClinical Solutions helps streamline trial operations and improve productivity, enabling better decision-making in clinical research.

📋 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

🎯 Requirements

• 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

🏖️ Benefits

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