Senior Software Engineer at Cryoport Systems in Remote - USA

Job description

• 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.

Responsibilities

• • 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.
• • 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.
• • 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.

Requirements

• • 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.

Skills

Required

• Ruby
• Java
• Python
• JavaScript
• MySQL
• Docker
• Elasticsearch
• GitHub
• JIRA
• AWS
• Ruby on Rails
• Scala

Preferred

• Fine-tuning techniques (LoRA, RLHF)
• MLOps tooling (MLflow, Weights & Biases)