Nucleus Labs

Our client is a US-based remote-online-learning provider serving students through live digital instruction and operational student-support workflows. Together with Nucleus AI, the client is building the CCA Wellness Check Compliance Platform — a focused internal system for recording, tracking, and proving weekly student wellness checks.

The platform is designed to solve a concrete compliance and operations problem: the school needs a reliable way to confirm that weekly student wellness checks happened, maintain an auditable record, identify students who were not seen, and support escalation workflows when follow-up is required.

The MVP is time-sensitive and must be production-ready before the August teacher training window and September operational launch.

You’ll join a delivery team building a practical compliance platform from the ground up, working closely with product, engineering, QA, delivery, and client stakeholders.

Responsibilities

Fullstack Product Development

• Build and maintain end-to-end product features across backend services and frontend application layers.
• Work with frontend frameworks such as React, Angular, or Blazor to deliver user-facing functionality.
• Integrate frontend applications with backend APIs and ensure smooth client-server communication.
• Debug issues across the full application stack, including frontend behavior, API contracts, backend logic, database interactions, and deployment environments.
• Collaborate with product, design, frontend, backend, and AI/ML teams to translate requirements into working product features.
• Ensure frontend and backend changes are aligned, tested, and production-ready.

Backend Development & Architecture

• Design and build scalable backend services using .NET Core / .NET Framework.
• Develop RESTful APIs and microservices using ASP.NET Core.
• Apply clean architecture principles and enforce coding standards.
• Ensure systems are performant, secure, and maintainable.

Cloud & Infrastructure

• Build and operate services on Microsoft Azure, including App Services, Functions, Azure SQL, and AKS.
• Implement CI/CD pipelines using Azure DevOps or GitHub Actions.
• Work with containerized environments such as Docker and Kubernetes.
• Ensure system reliability, monitoring, and disaster recovery strategies.

Code Quality & Performance

• Conduct code reviews and enforce best practices across backend and frontend codebases.
• Implement unit, integration, and end-to-end testing where appropriate.
• Monitor, profile, and optimize system performance across application layers.

Security & Compliance

• Implement secure authentication and authorization using OAuth, JWT, or similar approaches.
• Follow OWASP best practices.
• Ensure data protection and system integrity.

Documentation & Improvement

• Document technical decisions, API contracts, system behavior, and development workflows.
• Continuously improve system design and development workflows.
• Stay current with .NET, frontend, and cloud ecosystem developments.

Requirements

• 5+ years of professional engineering experience, with strong commercial experience in .NET / C#.
• Strong experience with ASP.NET Core, Web API, Entity Framework, and SQL Server.
• Hands-on frontend experience with React, Angular, Blazor, or a comparable modern frontend framework.
• Ability to build fullstack features from database and backend logic through API design and frontend implementation.
• Experience integrating frontend applications with REST APIs and debugging client-server issues.
• Hands-on experience with Microsoft Azure.
• Experience with CI/CD pipelines and Git.
• Experience with Docker and Kubernetes.
• Strong understanding of SDLC, SOLID, and design patterns.
• Experience with unit testing frameworks such as xUnit, NUnit, or MSTest.
• Ability to debug and resolve complex issues across the full application stack.
• Strong communication skills and ability to work closely with cross-functional teams.
   

Nice to Have

• Experience with React or AngularJS.
• Experience with messaging systems such as RabbitMQ, Kafka, or Azure Service Bus.
• Familiarity with NoSQL databases such as Cosmos DB or MongoDB.
• Azure certifications.
• Experience in LMS, edtech, legal-tech, fintech, or data-heavy platforms.
• Experience working with AI-assisted systems, AI/ML integrations, or data-driven product features.

What Matters for This Role

• Ownership mindset — ability to take responsibility for features and systems end-to-end.
• Strong backend engineering foundation combined with practical frontend delivery skills.
• Comfort working in ambiguous, fast-moving environments.
• Strong engineering judgment and pragmatic decision-making.
• Ability to collaborate across backend, frontend, cloud, product, and AI/ML teams.

We are seeking a highly capable Full Stack Engineer with deep experience in Python and React, who thrives on harness engineering—building the automation, testing scaffolding, execution environments, and orchestration layers that allow experimental prototypes to evolve into stable, production-ready MVPs.

This role is ideal for engineers who can fluidly move between backend services, frontend product development, integration tooling, and developer platforms. You’ll help transform early-stage prototypes into systems that are testable, observable, repeatable, and reliable at small-to-medium scale.

At this position, your work directly contributes to rapid experimentation: turning concepts built by the founder into scalable, deployable software.

Key Responsibilities

• Build and maintain full stack applications using Python on the backend and React on the frontend. 
• Design and implement APIs, services, and orchestration layers to support product workflows and internal tooling. 
• Develop harnesses for testing, simulation, validation, execution, and monitoring of complex workflows and agent-based systems. 
• Create reusable frameworks for:
• workflow execution
• automated test runs
• sandboxed environments
• synthetic data generation
• scenario replay
• regression validation
• Build developer-facing tools to improve engineering velocity, reliability, and repeatability. 
• Integrate external APIs, services, and third-party tools into internal platforms.
• Implement logging, tracing, observability, and error handling across systems.
• Collaborate with product, design, and platform stakeholders to turn experimental ideas into scalable technical solutions.
• Help define engineering standards for reliability, maintainability, and deployment readiness.

Required Qualifications

• Hands-on experience building production systems with Python. 
• Hands-on experience building modern frontend applications with React. 
• Experience designing and consuming REST APIs and/or async service architectures.
• Demonstrated experience in harness engineering: building tools for testing, validation, simulation, automation, or execution environments.
• Comfortable working across both application code and engineering infrastructure/tooling.
• Strong understanding of software design, debugging, and performance optimization.
• Experience with relational databases such as PostgreSQL, MySQL, or similar.
• Familiarity with CI/CD pipelines, Git-based workflows, and deployment practices.
• Ability to work independently, move quickly, and make sound engineering tradeoffs.

Preferred Qualifications

• Experience with workflow orchestration, agent-based systems, or automation pipelines.
• Experience with Docker, cloud infrastructure, and containerized environments.
• Familiarity with background job systems, queues, and event-driven architectures.
• Experience building internal platforms, QA tooling, evaluation frameworks, or developer productivity tools.
• Exposure to LLM/AI workflow orchestration, RPA, or test automation systems.
• Experience with observability tools, telemetry, and production monitoring.

What Success Looks Like

• You can ship polished frontend and backend features end-to-end. 
• You can build robust harnesses that make systems easier to test, measure, debug, and scale.
• You improve both product delivery and engineering leverage.
• You help the team move faster by making complex workflows repeatable and reliable.

Ideal Candidate Profile

You are more than a feature engineer; you think in systems and ask:
- How do we test this?
- How do we simulate this?
- How do we validate edge cases?
- How do we make this reproducible?
- How do we instrument this so it can be trusted in production?

You enjoy building the glue, scaffolding, and runtime support that transform experimental ideas into dependable products.  

Role Overview
We are looking for a highly capable Full Stack Engineer with deep experience in Python and React who is also very comfortable with harness engineering—building the surrounding tooling, automation, testing scaffolding, execution environments, and orchestration layers that allow systems, agents, workflows, and product features to be developed, validated, and run reliably at scale.

This role is ideal for an engineer who can move fluidly between backend services, frontend product development, automation frameworks, integration layers, and developer tooling. You should be equally comfortable shipping user-facing features and building the technical harnesses that make systems testable, observable, repeatable, and production-ready.

Key Responsibilities

• Build and maintain full stack applications using Python on the backend and React on the frontend.
• Design and implement APIs, services, and orchestration layers for product workflows and internal tools.
• Develop harnesses for testing, simulation, validation, execution, and monitoring of complex workflows, agents, or integrations.
• Create reusable frameworks for:
  • workflow execution
  • automated test runs
  • sandboxed environments
  • synthetic data generation
  • scenario replay
  • regression validation
• Build developer-facing tools to improve engineering velocity, quality, and repeatability.
• Integrate external services, APIs, and third-party tools into internal platforms.
• Implement logging, tracing, observability, and error-handling across systems.
• Collaborate with product, design, and platform stakeholders to turn business needs into scalable technical solutions.
• Help define engineering standards for reliability, maintainability, and deployment readiness.

Required Qualifications

• Strong hands-on experience building production systems with Python.
• Strong hands-on experience building modern frontend applications with React.
• Experience designing and consuming REST APIs and/or async service architectures.
• Experience with harness engineering, including building tooling for testing, validation, simulation, automation, or execution environments.
• Comfortable working across both application code and engineering infrastructure/tooling.
• Strong understanding of software design, debugging, and performance optimization.
• Experience with databases such as PostgreSQL, MySQL, or similar.
• Familiarity with CI/CD pipelines, Git-based workflows, and deployment practices.
• Ability to work independently, move quickly, and make sound engineering tradeoffs.

Preferred Qualifications

• Experience with workflow orchestration, agent-based systems, or automation pipelines.
• Experience with Docker, cloud infrastructure, and containerized environments.
• Familiarity with background job systems, queues, and event-driven architectures.
• Experience building internal platforms, QA tooling, evaluation frameworks, or developer productivity tools.
• Exposure to LLM/AI workflow orchestration, RPA, or test automation systems.
• Experience with observability tools, telemetry, and production monitoring.

What Success Looks Like

• You can ship polished frontend and backend features end-to-end.
• You can build robust harnesses that make systems easier to test, measure, debug, and scale.
• You improve both product delivery and engineering leverage.
• You help the team move faster by making complex workflows repeatable and reliable.

Ideal Candidate Profile
You are not just a feature engineer. You think in systems. You naturally ask:

• How do we test this?
• How do we simulate this?
• How do we validate edge cases?
• How do we make this reproducible?
• How do we instrument this so we can trust it in production?

You enjoy building the “glue,” scaffolding, and runtime support that turn promising ideas into dependable products.

Project Context

Our client is building and maintaining software systems where reliability, data protection, and secure user flows are critical. The QA function is not limited to checking whether features work as expected. The team also needs someone who can actively challenge the system, identify weak points, verify security assumptions, and help prevent vulnerabilities before they reach production.

We are looking for an Ethical Hacker / Penetration Tester who can work closely with QA (or as a part time QA), engineering, and product teams to test web applications, APIs, authentication flows, permissions, infrastructure exposure, and overall application security.

This is a hands-on role. The main expectation is to find real risks, reproduce them clearly, document them properly, and help the engineering team validate that fixes are effective.

Role

We are looking for a Security QA Engineer / Ethical Hacker / Penetration Tester who will take ownership of security testing across application, API, and platform layers.

This person should be comfortable working as part of a QA-oriented delivery process while bringing deeper security expertise into the team. The role requires practical knowledge of penetration testing, vulnerability assessment, secure testing methods, and clear communication with developers.

Key Responsibilities

• Perform manual security testing of web applications, APIs, user flows, and admin panels.
• Conduct penetration testing against staging and production-like environments.
• Identify, reproduce, document, and validate security vulnerabilities.
• Test authentication, authorization, permissions, session management, password flows, and access-control logic.
• Review API endpoints for broken access control, injection risks, data exposure, rate-limit issues, and insecure design.
• Validate security fixes and perform regression security testing.
• Work with QA engineers to include security checks in the regular testing process.
• Prepare clear reports with reproduction steps, evidence, severity, impact, and recommended remediation.
• Collaborate with developers to explain vulnerabilities and verify that fixes address the real issue.
• Support threat modeling and security review of new features when needed.
• Help improve security awareness and testing standards inside the team.

Required Experience

• 3+ years of experience in penetration testing, application security, security QA, or ethical hacking.
• Strong understanding of OWASP Top 10 and common web application vulnerabilities.
• Experience testing REST APIs and web-based systems.
• Practical experience with authentication and authorization testing.
• Ability to discover, reproduce, and explain vulnerabilities clearly.
• Experience using security testing tools such as Burp Suite, OWASP ZAP, Nmap, Wireshark, Metasploit, or similar.
• Understanding of HTTP, HTTPS, cookies, headers, sessions, JWT, OAuth2, MFA, and role-based access control.
• Ability to write simple scripts or proof-of-concept checks when needed, preferably in Python.
• Strong documentation skills and ability to produce actionable vulnerability reports.
• Good communication skills and ability to work directly with engineering and QA teams.
• Upper-intermediate or better English for written and spoken communication.

Nice to Have

• Experience with cloud security testing, especially AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure security basics.
• Experience with mobile application security testing.
• Experience with secure code review.
• Experience with SAST, DAST, dependency scanning, and security automation.
• Bug bounty experience or responsible disclosure background.
• Security certifications such as OSCP, PNPT, eJPT, CEH, GWAPT, or similar.
• Experience working in product teams rather than only external consulting environments.

What Success Looks Like

• Security issues are found early and described clearly.
• Developers can reproduce and fix reported vulnerabilities without confusion.
• QA coverage becomes stronger because security checks are included in regular delivery.
• Authentication, authorization, and API risks are actively tested before release.
• Security findings are prioritized by real impact, not just tool output.
• The team gains practical confidence that critical user flows and sensitive data are protected.

Nucleus Labs is building AI-powered products across multiple domains, with a strong focus on practical, production-grade systems that leverage modern LLM and machine learning capabilities.

Our engineering teams are actively developing AI-assisted workflows, document-processing pipelines, extraction systems, and human-in-the-loop platforms. While our ML researchers and AI engineers focus on experimentation, model evaluation, and solution discovery, we are looking for an engineer who can transform these solutions into reliable, maintainable, and production-ready systems.

This role sits at the intersection of Software Engineering, MLOps and AI Platform Engineering.

You will act as the bridge between ML research, product engineering, architecture, and infrastructure teams, ensuring that AI solutions move beyond local experimentation and become scalable, observable, and maintainable production systems.

Responsibilities

AI Platform & MLOps Engineering

• Productionalize AI and ML solutions developed by research teams and integrate them into customer-facing products.
• Build and maintain reusable, maintainable, and extensible AI pipelines.
• Design systems that support rapid iteration of AI workflows without requiring full system rewrites or redeployments.
• Implement and maintain human-in-the-loop feedback workflows for AI-assisted products.
• Design mechanisms for collecting feedback, annotations, evaluation data, and model performance signals.

AI Workflow Development

• Build and orchestrate complex LLM-based workflows involving multiple inference steps, tools, and decision points.
• Extend and maintain pipelines involving LLM calls, OCR systems, extraction engines, and data-processing workflows.
• Collaborate with ML engineers to integrate new models, prompts, evaluation approaches, and AI capabilities.
• Ensure AI pipelines remain easy to evolve as business and product requirements change.

Software Engineering

• Design, develop, and maintain backend services that expose AI capabilities through internal and external APIs.
• Build clean, modular, and maintainable software using modern engineering practices.
• Write production-quality code with strong emphasis on readability, testability, and long-term maintainability.
• Participate in architecture discussions and technical design reviews.
• Debug and resolve issues across application, infrastructure, and AI workflow layers.

MLOps & Infrastructure Collaboration

• Work closely with DevOps engineers to design infrastructure required for AI workloads.
• Define infrastructure requirements for deployment, storage, compute, observability, and scaling.
• Collaborate on CI/CD pipelines supporting continuous delivery of AI-enabled systems.
• Contribute to infrastructure-as-code requirements and collaborate on Terraform-based environments.
• Help determine appropriate cloud services and deployment strategies for AI workloads.

Model Lifecycle & Data Operations

• Understand and support the full ML lifecycle, including:
  • data collection
  • annotation workflows
  • training
  • validation
  • evaluation
  • deployment
  • monitoring
  • continuous improvement
• Design systems that support future model fine-tuning and retraining initiatives.
• Build infrastructure and automation around dataset management, evaluation, and reporting.

Observability & Reliability

• Implement monitoring, logging, tracing, and alerting for AI systems.
• Define metrics that measure model quality, pipeline health, and business outcomes.
• Ensure production AI systems are observable, debuggable, and resilient.
• Participate in incident investigation and root cause analysis for AI-related production issues.

Requirements

• 5+ years of professional software engineering experience.
• Strong commercial experience building backend systems using Python.
• Strong software engineering fundamentals, including clean architecture, SOLID principles, design patterns, and SDLC best practices.
• Experience building and maintaining production-grade distributed systems.
• Hands-on experience integrating LLMs and AI services into production applications.
• Practical understanding of MLOps concepts and the machine learning lifecycle.
• Experience designing and implementing CI/CD pipelines.
• Experience working in cloud environments, preferably AWS.
• Experience working with Docker and containerized applications.
• Experience collaborating with DevOps teams on infrastructure and deployment topics.
• Ability to understand, maintain, and evolve AI/ML codebases created during research and experimentation phases.
• Experience building maintainable APIs and backend services.
• Strong debugging and troubleshooting skills across application and infrastructure layers.
• Strong communication skills and ability to collaborate across cross-functional teams.

Nice to Have

• Experience with AWS services such as ECS, EKS, EC2, Lambda, S3, Step Functions, Bedrock, or SageMaker.
• Experience with workflow orchestration frameworks such as Temporal, Airflow, Dagster, Prefect, or similar.
• Experience with vector databases and retrieval systems.
• Experience with OCR systems and document-processing pipelines.
• Experience fine-tuning open-source models.
• Experience building human-in-the-loop systems.
• Experience with model evaluation frameworks and LLM observability platforms.
• Experience with Terraform or Infrastructure as Code.
• Experience with Kubernetes.
• Experience working with AI agents, agent orchestration frameworks, or tool-calling systems.
• Experience in document AI, legal-tech, healthcare, edtech, or other data-heavy domains.
• Experience working with open-source ML ecosystems.

What Matters for This Role

• Ownership mindset — ability to take responsibility for AI-enabled systems end-to-end.
• Strong software engineering foundation with practical understanding of ML systems.
• Ability to bridge the gap between ML research and production engineering.
• Pragmatic decision-making and strong engineering judgment.
• Comfort working in ambiguous, fast-moving environments.
• Ability to design systems that are maintainable, extensible, and easy to evolve.
• Curiosity about AI systems combined with strong software craftsmanship.
• Willingness to work across traditional engineering, AI, infrastructure, and product boundaries.