Software Engineer

Remote Hiring Remotely in USA Senior level Remote Hiring Remotely in USA Senior level Leads software architecture and delivery while building scalable web applications and production-grade AI systems. Responsibilities include LLM integrations, RAG pipelines, vector search, model evaluation, ML serving, AI observability, APIs, microservices, and cloud deployment. The role contributes to technical roadmaps, architecture reviews, agile initiatives, documentation, and domain-driven design while mentoring engineers and collaborating with product, design, data science, and engineering teams. The summary above was generated by AI

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., 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.

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., 1. Technical Expertise

• Implement 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 and ensure best practices in software development, including modularization, code quality, testing, security and data modeling.
• Establish engineering best practices around prompt management, AI evaluation frameworks, and observability for AI systems.

1. AI Engineering * Design and build production-grade AI systems, integrating foundation models (e.g., OpenAI, Anthropic) via frameworks such as LangChain or LlamaIndex. * Architect retrieval-augmented generation (RAG) pipelines and vector search solutions using tools like Pinecone, Weaviate, or pgvector. * Evaluate and benchmark foundation models and open-source alternatives for suitability, performance, and cost-efficiency. * Design and maintain scalable ML serving infrastructure and model deployment pipelines. * Collaborate with data science teams to bring model-driven capabilities to production reliably.

2. 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.

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

• Support and mentor mid-level engineers, fostering a culture of learning, technical ownership, and responsible AI development.
• Work closely with product managers, designers, and engineering teams to align technical efforts with business goals.

Tooling/Technologies Ruby, Java, Python, JavaScript, MySQL, Docker, Elasticsearch, GitHub, JIRA, AWS Ruby on Rails, Scala, Micronaut, React LLM APIs (OpenAI, Anthropic), LangChain, LlamaIndex, vector databases, RAG pipelines, Develop cross-platform device management software for Windows, macOS, and Linux using Go and Node.js. Build endpoint management engines, system agents, cloud APIs, MDM telemetry, and zero-touch enrollment pipelines. Work with operating system internals, authentication protocols, cloud infrastructure, CI/CD, Kubernetes, and automated testing. Collaborate with product, UX, architecture, and DevOps teams in a Scrum environment, contribute to planning, mentor junior engineers, and participate in on-call rotations. Top Skills: AndroidAWSAzureC#C++Ci/CdDeclarative Device ManagementDockerGCPGithub ActionsGoiOSJavaKubernetesLinuxmacOSMdmMtlsNode.jsOauthOidcPythonScrumSwiftWindows Engine, Lead technical direction for a central infrastructure team supporting all product engineering. Design and maintain AWS infrastructure, Terraform modules, CI/CD workflows, observability, security, networking, cost-visibility tooling, and operational platforms. Coordinate multiple initiatives, improve reliability and incident response, establish paved roads adopted by feature teams, guide infrastructure strategy, and coach engineers across the organization., Automate tests for web-based and mobile applications and RESTful APIs supporting high-transaction, big-data SaaS products. Drive high-quality releases, apply quality standards, support SDLC processes, solve complex problems, and promote a customer-first quality culture across the organization. Top Skills: Crm SystemsErp SystemsMobile ApplicationsPayment SystemsPoint-Of-Sale SystemsRestful ApisSaaS

What you need to know about the Colorado Tech Scene

With a business-friendly climate and research universities like CU Boulder and Colorado State, Colorado has made a name for itself as a startup ecosystem. The state boasts a skilled workforce and high quality of life thanks to its affordable housing, vibrant cultural scene and unparalleled opportunities for outdoor recreation. Colorado is also home to the National Renewable Energy Laboratory, helping cement its status as a hub for renewable energy innovation.

Key Facts About Colorado Tech

• Number of Tech Workers: 260,000; 8.5% of overall workforce (2024 CompTIA survey)
• Major Tech Employers: Lockheed Martin, Century Link, Comcast, BAE Systems, Level 3
• Key Industries: Software, artificial intelligence, aerospace, e-commerce, fintech, healthtech
• Funding Landscape: $4.9 billion in VC funding in 2024 (Pitchbook)
• Notable Investors: Access Venture Partners, Ridgeline Ventures, Techstars, Blackhorn Ventures
• Research Centers and Universities: Colorado School of Mines, University of Colorado Boulder, University of Denver, Colorado State University, Mesa Laboratory, Space Science Institute, National Center for Atmospheric Research, National Renewable Energy Laboratory, Gottlieb Institute

• 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 Ruby (Ruby on Rails), J2EE, JavaScript (React, Node), Python, 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.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) and frameworks such as LangChain, LlamaIndex, or equivalent.
• Experience with vector databases (Pinecone, Weaviate, pgvector), semantic search architectures.
• Familiarity with ML serving infrastructure and cloud-based AI deployment patterns.
• Experience with model evaluation, prompt engineering, and AI observability tooling.
• Knowledge of fine-tuning techniques (LoRA, RLHF) or MLOps tooling (MLflow, Weights & Biases) is a plus.

Key Competencies

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