Staff Software Engineer (Contingent)
Cryoport, Inc.2 months ago
Remote, United StatesStaff+
Responsibilities
- Architect, design, implement, evolve, and support scalable, resilient, and maintainable software systems.
- Lead value-stream initiatives, architecture reviews, roadmap planning, and technical vision development.
- Build and productionize AI/ML and LLM-powered features, including retrieval-augmented generation and agentic workflows.
- Establish software development practices covering modularization, code quality, testing, security, data modeling, observability, and cost/performance tradeoffs.
- Collaborate with product managers, designers, stakeholders, platform teams, and infrastructure teams to translate business objectives into technical requirements and improve deployment tooling and cloud environments.
- Mentor mid-level and senior engineers and promote technical ownership, knowledge sharing, and responsible AI development.
- Advocate for Domain-Driven Design and loosely coupled architectures while evaluating new technologies and tools.
Requirements
- Bachelor’s degree in Computer Science or an equivalent degree is required; a master’s degree in Computer Science is preferred.
- 10+ years architecting, implementing, and maintaining large multi-tier distributed web applications using Ruby, Ruby on Rails, J2EE, JavaScript, React, Node, Python, and other web technologies.
- 6+ years architecting, implementing, and maintaining JSON APIs.
- Extensive knowledge of microservices, APIs, event-driven architectures, containerization, Docker, Kubernetes, and data modeling.
- 2+ years of hands-on AI/ML engineering experience in production environments.
- Hands-on experience with LLM APIs such as OpenAI and Anthropic, and frameworks such as LangChain or LlamaIndex.
- Experience with vector databases such as Pinecone, Weaviate, or pgvector and semantic search architectures.
- Familiarity with ML serving infrastructure, cloud-based AI deployment patterns, model evaluation, prompt engineering, and AI observability tooling.
- Knowledge of fine-tuning techniques such as LoRA and RLHF or MLOps tools such as MLflow and Weights & Biases is preferred.
- Strong analytical problem-solving, troubleshooting, collaboration, strategic vision, innovation, automation, and practical judgment regarding AI/LLM use.
Tech Stack
Categories