Senior/Principal Full-stack Engineer

Location: REMOTE / Kitchener-Waterloo, Ontario. This job allows you to work remotely. Role Overview You will own their AI and full-stack engineering efforts You will shape next generation features that help scientists run experiments faster You will guide the platform's scalability and drive new integrations for lab instruments How will you spend your time? 50% coding and system design (React, Python, Java + AI integration) 20% product iteration and user feedback loops 10% collaboration, planning, and roadmap refinement 10% data engineering, infrastructure and embedding strategies 10% LLM experimentation (prompting, AI pipelines, graph DBs, vector DBs) What You’ll Do Architect and Scale: Build robust backend services with intuitive UI/UX (React, Java Spring Boot, AWS, Kubernetes). Develop new AI-based features for enterprise customers. Elevate the AI Stack: Enhance recommendation engines with prompt engineering and LLMs. Building AI pipelines with LLMs. Introduce NLP for seamless instrument integration. Drive Quality and Automation: Implement automated tests. Oversee telemetry improvements. Lead and Mentor: Collaborate with product, data, and design teams. Grow a team of engineers focused on cutting-edge AI tools. Your Two Year Roadmap Month 1-6, you will: Enhance Recommendation AI: Use prompt engineering and AI pipelines with LLMs for better suggestions. Aim for performance and scalability. Scale API and GLUE Layer: Build strong ETL support for enterprise loads. Build SDK framework for Scispot APIs. Introduce NLP for Instrument Integration. Offer script templates so scientists can process data easily. Suggest Telemetry Improvements: Improve monitoring for infrastructure health. Graphical Chain of Custody: Let users query sample journeys with prompts using graph database. Month 7-12, you will: EKS Migration: Grow & Maintain AWS EKS cluster. Automated Testing: Increase backend unit test coverage. MCP Layer for Recommendation: Allow AI agents to take simple actions for scientists. Upgrade Search: Improve OpenSearch and vector databases. Memory Layer for Agents: Reduce reliance on retrieval-augmented generation by building memory layer for AI agents. Month 13-24, you will: Lead Core Application Team: Oversee tech vision, architecture, and development. App Store for Instrument Connectors: Expose our instrument integrations in a user-friendly marketplace. Special Perks Why join them? Work from anywhere but ideally based out of Canada. Engage in challenging, impactful work in the realm of biotech data and AI. Competitive stock options. Unlimited growth upside. Why You Might Love This Role You want to shape the future of scientific research. You enjoy solving complex AI challenges. You like leading from the front, mentoring, and guiding teams. A chance to build next-gen AI tools for lab workflows. Leadership role with a high level of autonomy. Why You Might Not You dislike fast-paced startup environments. You prefer strictly defined roles. Must Have Skills Proficiency in Java, Python, React & JavaScript Experience deploying to AWS (EKS, Lambda, or EC2). Deep knowledge of AI pipelines, LLMs, and NLP libraries. Familiarity with data stores (OpenSearch, vector databases, graph databases). Strong leadership and communication skills. Tech Stack Frontend: React JS and Typescript Backend: Elastic Search, AWS Lambda, Rabbit MQ, Mongo DB, S3, Java Spring Boot Architecture: Microservices integrated with GraphQL and Rest APIs AI Infrastructure: TensorFlow (Proprietary ML), Azure AI Service, Azure Open AI service, AI Pipelines, Programmatic Prompt Engineering Nice to Have Skills Experience with scientific or biotech workflows. Knowledge of advanced ETL, data streaming, or prompt engineering. Ideal Candidate Profile Proficient with AWS and its suite of data services. Hands-on experience with tools such as Lambda function, MQ, Java spring boot, Elastic Search, Python, Mongo DB, Dynamo DB, and S3 bucket. Strong programming skills, particularly in Python, Java, React & JavaScript. Good understanding of different Agentic AI architectures. Good understanding of learning how to build AI pipelines with LLMs. A solid grasp of microservices and associated best practices. Experience in data engineering and orchestration is preferred. Loves working in a fast paced startup environment. #J-18808-Ljbffr