Principal AI Full-Stack Developer

BISSELL Homecare•Grand Rapids, MI

About The Position

We are seeking a Principal AI Full-Stack Developer with a strong foundation in software engineering to design, develop, and deploy intelligent applications that leverage AI capabilities across the enterprise technology stack. This role is ideal for an experienced engineer with deep expertise in programming logic, application architecture, and modern development frameworks, who can seamlessly integrate AI into scalable, production-grade systems. The position combines advanced full-stack development skills with a strong understanding of AI integration, including embedding LLM’s, agents, and RAG connections across web and mobile applications. This role will own end-to-end application development, from front-end experiences and back-end services to AI orchestration, APIs, and cloud deployment. You will partner closely with enterprise architects, product managers, automation teams, and data scientists to embed AI into real business workflows and accelerate AI-first engineering practices across the organization.

Requirements

Enterprise Cloud Architecture & Strategy: Expertise in designing, implementing, and optimizing cloud-based architectures at scale.
Technical Leadership & Mentorship: Proven ability to lead engineering teams and mentor talent across multiple disciplines.
Automation & Infrastructure as Code: Strong background in automating cloud environments using Terraform, CloudFormation, or similar tools.
Security & Compliance: Deep understanding of cloud security frameworks, regulatory compliance, and risk management.
Problem-Solving & Decision-Making: Ability to analyze complex technical issues, provide innovative solutions, and drive decision-making.
Strategic Vision & Business Alignment: Ability to align AI + cloud strategies with business goals and drive digital transformation.
Collaboration & Communication: Exceptional ability to communicate technical concepts to both engineering teams, business owners and executive leadership.
Continuous Learning & Innovation: Passion for staying ahead of AI & cloud technology trends and industry advancements.
Bachelor’s or master’s degree in computer science, Software Engineering, Information Technology, or a related field.
Equivalent real-world experience may be considered in place of a degree.
15+ years of experience in software development, cloud architecture, and DevOps.
1+ year of hands-on AI engineering experience, ideally in an enterprise or startup setting.
Proven experience with AI development tools (e.g., Claude, GPT-4, etc).
Experience with AI/ML frameworks or services (e.g., OpenAI, Azure OpenAI, TensorFlow, PyTorch).
Experience with AI agents, agentic workflows and autonomous AI systems.
Experience deploying applications and AI workloads to cloud environments.
Strong understanding of application architecture, APIs, and microservices.
Contributions to open-source AI projects or internal AI platforms.
Deep expertise in cloud platforms (AWS, Azure, Google Cloud).
Strong proficiency in programming languages
Strong expertise in CI/CD automation (Jenkins, GitHub Actions, GitLab CI/CD).
Experience with cloud security, IAM policies, encryption, and compliance frameworks.
Strong background in API development, integration, and microservices architecture.
Experience with cloud networking (VPCs, load balancers, DNS, VPNs).
Proficiency in monitoring, logging, and observability (CloudWatch, Prometheus, Datadog).
Strong ability to lead and influence technical discussions across the organization.

Nice To Haves

AWS Certified Solutions Architect – Professional or AWS DevOps Engineer – Professional
Microsoft Certified: Azure Solutions Architect Expert
Agent Framework Training Certificates
Certified Kubernetes Administrator (CKA) or Certified Kubernetes Security Specialist (CKS)

Responsibilities

Own the full lifecycle of AI-enabled solutions, from ideation and architecture to deployment and ongoing optimization.
Design, develop, and maintain full-stack applications that integrate AI/LLM capabilities into user-facing and backend systems.
Architect and implement AI integrations using platforms and frameworks such as OpenAI, Azure OpenAI, AWS Bedrock, Gemini, and similar orchestration frameworks.
Influence long-term AI roadmap decisions, advising executive leadership (VP/CIO, CTO) on platform strategy, model lifecycle planning, and capability investments.
Leadership of an AI Engineering Guild and Community of Practice. Mentor and coach developers, analysts, and automation engineers on AI-first engineering patterns, tools, and best practices,
Work closely with Enterprise Architecture and co-lead design reviews and approve architectural decisions related to AI systems, data flows, security boundaries, and integration patterns.
Build, deploy, and maintain AI agents as part of full-stack, production-grade software systems.
Integrate AI agents with all development and operational tools (e.g., Linear, GitHub, Datadog, Sentry, internal platforms) to maximize context and productivity. Proactively address and resolve any access barriers that limit agent effectiveness.
Build scalable backend services using Node.js and Python.
Develop modern, responsive front-end applications using React.
Design and maintain RESTful and event-driven APIs that expose AI-driven functionality.
Collaborate with Automation and Platform teams to embed LLMs into automated workflows and enterprise processes.
Apply strong software engineering fundamentals (data structures, algorithms, design patterns, and clean architecture) when building AI-enabled systems.
Ensure all AI-powered features leverage the latest generation models, migrating off legacy models as soon as robust evaluations support the change. Maintain a rapid upgrade cadence to maximize product performance and value.
Lead technical enablement efforts by creating reusable AI components, SDKs, internal tooling, and reference architecture.
Champion AI engineering best practices, including prompt engineering, evaluation strategies, versioning, observability, and responsible/ethical AI use.
Support cloud-native deployment of AI-enabled applications across AWS, Azure, or GCP environments.
Architect and oversee the implementation of advanced AI solutions, including multi-agent systems and generative AI platforms.
Lead technical due diligence for new AI technologies and vendors.
Implement embedding-based semantic search across all product surfaces, replacing legacy fuzzy search algorithms for superior relevance and accuracy.
Stay current with emerging AI technologies and assess their applicability to enterprise software solutions.
Deliver production-ready, maintainable, and secure AI-enabled applications aligned with enterprise standards.
Ensure AI solutions meet requirements for availability, performance, scalability, security, and compliance.
Drive standardization of AI development patterns, deployment pipelines, and operational practices.
Empower engineering teams to select and utilize the most effective coding agents and harnesses—including Claude, Cursor, Devin, and both open and closed models. Ensure all engineers have access to the latest agent technologies and are not restricted by tooling or context limitations.
Influence and implement cloud governance, security, and compliance strategies related to AI workloads.
Champion an AI-first engineering culture and mentor junior engineers and contribute to internal standards, documentation, and knowledge-sharing initiatives.
Foster an AI-first engineering culture by building primitives for model invocation and sandboxed code execution.
Invest in comprehensive agent documentation tailored to your codebase. Continuously improve agent.md files, linting rules, and prompt strategies to optimize agent performance and minimize manual interventions.
Prioritize advanced prompt engineering over custom model finetuning, adapting quickly to frontier model improvements and cost reductions.