Cryoport Systems is a comprehensive supply chain partner for the life sciences industry, delivering specialized solutions to meet the challenges of the biopharmaceutical, cell and gene therapy, reproductive medicine, and animal health markets. Our offerings span logistics, BioServices and biostorage, cryopreservation, and consulting, ensuring the highest standards of quality and reliability for sensitive materials. With our expansive platform of management solutions and decades of temperature-controlled supply chain expertise, Cryoport Systems helps Enable the Outcome™  by supporting certainty and precision in the supply chain, whether advancing groundbreaking therapies, supporting families on their reproductive journeys, or enhancing animal health programs.

POSITION SUMMARY

As a Senior Software Engineer at Cryoport Systems, you will lead technical initiatives, optimize team delivery, and drive architectural decisions within one of our teams. You will collaborate closely with the technical leadership team to ensure deliverables align with the organization’s broader goals and support our clients’ needs, including the design and delivery of AI-powered capabilities. You will be responsible for building, evolving and supporting core value stream application components — including AI-powered features and intelligent systems — essential for our organization’s growth. Your leadership will ensure system success and foster technical excellence, allowing Cryoport Systems to deliver certainty and reliability across our business.

Key Responsibilities:
1. Technical Expertise

• Design, develop, and maintain scalable, resilient, and maintainable software systems aligned with technical roadmaps, including AI-powered features such as LLM integrations, RAG pipelines, and agentic workflows.

• Execute value stream initiatives in an agile environment, ensuring that features meet business and technical goals.

• Apply engineering best practices in software architecture, code quality, testing, security, observability, and data management.

• Establish engineering best practices around AI-driven development lifecycles, prompt management, AI evaluation frameworks, and observability for AI systems.

2. AI & Intelligent Systems Engineering

• Design, build, and support production-grade AI-enabled applications and intelligent business capabilities.

• Develop solutions that leverage modern AI technologies, machine learning services, retrieval systems, and foundation models where appropriate.

• Evaluate emerging AI platforms, frameworks, and tools to determine their suitability, effectiveness, scalability, and business value.

• Design and implement knowledge-driven and retrieval-based solutions that improve decision-making, automation, and user experiences.

• Collaborate with data, product, and engineering teams to deliver AI-driven capabilities into production environments.

• Contribute to enterprise-wide standardization of AI development practices, governance, observability, and operational excellence.

• Ensure AI-enabled solutions align with organizational security, compliance, and responsible AI principles.

3. Systems Thinking and Innovation

• Participate in technical discussions, architecture reviews, and roadmap planning.

• Contributed to the technical vision and architecture for the stream.

• Advocate for Domain-Driven Design (DDD) and loosely coupled architectures.

• Review and assess new technologies, frameworks, and tools to enhance efficiency and scalability.

4. Stream Alignment

• Collaborate with product managers and stakeholders to understand business goals and translate them into technical requirements, including AI-driven capabilities.

• Contribute to technical documentation and knowledge sharing across teams.

• Ensure the team’s work aligns with objectives laid out no technical roadmaps.

Team Leadership and Collaboration:

• Mentor and support engineers across the organization, fostering a culture of learning, accountability, and technical excellence.

• Encourage knowledge sharing and collaborative problem solving across teams.

• Partner closely with product managers, designers, architects, data professionals, and engineering teams to align technical efforts with business objectives.

• Provide technical guidance and leadership while empowering team members to grow their skills and capabilities.

• Champion continuous learning and professional development within the engineering organization.

Tooling/Technologies:

Ruby, Python, JavaScript, TypeScript, Java, MySQL, Docker, Elasticsearch, GitHub, JIRA, AWS

Ruby on Rails, Scala, Micronaut, React, Django, FastAPI

LLM APIs (OpenAI, Anthropic), LangChain, LlamaIndex, vector databases, RAG pipelines

Required Qualifications:

• B.S. in Computer Science or equivalent degree (required) / M.S. in Computer Science (preferred)

• 8+ years architecting, implementing, and maintaining 100,000+ lines of code multi-tier distributed web applications using Python, JavaScript (React, Node), Ruby (Ruby on Rails), J2EE, and other web technologies.

• 5+ years architecting, implementing, and maintaining JSON API’s.

• 2+ years of hands-on AI/ML engineering experience in production environments.

• Extensive knowledge of microservices, APIs, event-driven architectures, containerization (Docker, Kubernetes), and data modeling.

• Experience incorporating AI-enabled capabilities, intelligent automation, analytics, or machine learning solutions into business applications.

• Experience with model evaluation, prompt engineering, and AI observability tooling.

Preferred Qualifications:

• Advanced degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field.

• Experience implementing AI-enabled applications in production environments.

• Experience with cloud-native architectures, microservices, event-driven systems, and modern platform engineering practices.

• Experience evaluating and adopting emerging technologies.

• Experience supporting highly available, mission-critical business systems.

• Knowledge of AI governance, observability, model evaluation, or responsible AI practices.

• Experience leading technical initiatives, mentoring engineers, and influencing architectural direction.

Key Competencies:

• Problem-Solving: Strong, detail-oriented analytical skills and a hands-on approach to troubleshooting and resolving technical challenges.

• AI Fluency: Practical, hands-on judgment about where AI/LLM-based approaches add real value versus traditional engineering, with an eye toward reliability, cost, and responsible use.

• Innovation: A forward-thinking mindset, constantly looking for ways to innovate and improve operations; passion for automating processes.

• Strategic Vision: Ability to align stream initiatives with broader business objectives.

• Collaboration: Excellent interpersonal skills and ability to collaborate across functional teams.