Note: The job is a remote job and is open to candidates in USA. Cryoport Systems is a life sciences supply chain partner providing logistics, BioServices, biostorage, cryopreservation, and consulting solutions. The Senior Software Engineer will lead technical initiatives, make architectural decisions, and build and support core applications, including AI-powered features and intelligent systems. The role also involves designing production-grade AI systems, guiding engineering practices, collaborating across teams, and mentoring engineers.
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
- 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
- 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
Skills
- B.S. in Computer Science or equivalent degree (required)
- 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
- 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
- M.S. in Computer Science (preferred)
- Knowledge of fine-tuning techniques (LoRA, RLHF) or MLOps tooling (MLflow, Weights & Biases) is a plus
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