Principal Engineer – AI and Full Stack

Scispot is one of Canada's fastest-growing startups, drawing the blueprint for the future of biotech data. Often referred to as the "Snowflake of biotech data," our mission is to develop cutting-edge data infrastructure for biotech labs globally. By blending technology with biotechnology, Scispot is making every lab an AI Powerhouse. With us, you have a chance to make a real impact, craft the digital spine of global biotech labs, and benefit from generous stock options. Scispot is building the digital backbone for scientific discovery. We empower biotech teams by unifying lab operations, data flow, and AI-driven insights. We are hiring a Principal Engineer – AI and Full Stack to lead an R&D scientific innovation project aimed at advancing Scispot’s AI-powered Lab Operating System. The role will focus on designing, prototyping, and commercializing new features that integrate AI, data science, and full-stack engineering to improve biotech lab workflows.

Key Activities

Lead research and development of AI-enabled modules for data capture, analysis, and visualization in life science labs.
Prototype and test new product features with pilot customers to validate technical feasibility and market fit.
Conduct technical and market analysis to optimize adoption of AI-driven lab tools.
Build and refine full-stack architectures that integrate with lab instruments, APIs, and cloud-based platforms.
Deliver measurable outcomes, such as reducing manual data entry time and increasing workflow automation in pilot labs.

Outcomes & Impact

Creation of innovative, market-ready tools that enhance lab efficiency and data quality.
Contribute to commercialization efforts through pilot deployments and adoption in Canadian and international labs.

Role Overview

You will own our AIand full-stack engineering efforts
You will shape next generation features that help scientists run experiments faster
You will guide our 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, AIpipelines, graph DBs, vector DBs)

What You9ll 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 Our AI Stack

Enhance recommendation engines with prompt engineering and LLMs. Building AIpipelines 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.

Required 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

Bonus Skills

Experience with scientific or biotech workflows
Knowledge of advanced ETL, data streaming, or prompt engineering

Your Two Year Roadmap

Month 1-6:

Enhance Recommendation AI: Use prompt engineering and AIpipelines 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:

EKS Migration: Grow &Maintain AWSEKScluster.
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 AIagents.

Month 13-24:

Lead Core Application Team: Oversee tech vision, architecture, and development.
App Store for Instrument Connectors: Expose our instrument integrations in a user-friendly marketplace.

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

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 , S3 bucket .
Strong programming skills in Python , Java , React & Javascript .
Good understanding of different Agentic AI architectures .
Good understanding of building AI pipelines with LLMs .
Solid grasp of microservices best practices .
Experience in data engineering and orchestration preferred .
Enjoys working in fast paced startup environment .

Why Join Scispot?:

Work from anywhere but ideally based out of Canada .
Engage in challenging impactful work in biotech data & AI realm .
Competitive stock options .
Unlimited growth upside .

Why You Might Love This Role

Shape the future of scientific research .
Solve complex AI challenges .
Lead from the front ; mentor & guide teams .
Build next-gen AI tools for lab workflows .
Leadership role with high autonomy .

Why You Might Not

Dislike fast-paced startup environments .
Prefer strictly defined roles .

Location: Work from anywhere but ideally based out of Canada . Employment Type: Not explicitly mentioned . Experience Level: Implied senior level due to "Principal Engineer" title ; leadership responsibilities included . Salary Information: Not mentioned ; mentions competitive stock options . Skills Mentioned: Java ; Python ; React ; Javascript ; AWS (EKS,Lambda or EC2) ; Kubernetes ; Java Spring Boot ; Elastic Search ; Rabbit MQ ; Mongo DB ; S3 ; TensorFlow (Proprietary ML) ; Azure AI Service ; Azure OpenAI service ; AI Pipelines ; Programmatic Prompt Engineering ; LLMs ; NLP libraries ; OpenSearch ; vector databases ; graph databases ; microservices architecture ; GraphQL & REST APIs ; prompt engineering ; ETL processes ; data streaming . ---