AI Engineer

Kandji

full-time

Posted on: 9/16/2025

Origin: • 🇺🇸 United States • Florida

✨ AI Apply

Mid-LevelSenior

AirflowAWSCloudDistributed SystemsDockerGoGoogle Cloud PlatformKubernetesMicroservicesPythonPyTorchRayScikit-LearnServiceNowTensorflowTypeScript

About the role

Architect, design, and implement scalable AI platform services and components that enable intelligent agents and LLM-powered applications.
Apply LLMs and other AI technologies directly to build and enhance Kandjiʼs intelligent features.
Build LLM-powered agents that autonomously reason through complex workflows.
Develop full-stack product surfaces (backend & frontend) that incorporate AI into user experiences.
Create reusable infrastructure, frameworks, and services for experimentation, deployment, and monitoring of AI at scale.
Implement scalable APIs and microservices that expose AI models and services to product teams.
Integrate with multiple LLM providers and manage model selection, routing, and fallback strategies.
Collaborate with Product Engineering, Data Science, frontend, and research teams to deliver secure, reliable AI experiences.
Partner with core services infrastructure team to support execution, monitoring, logging, and automated evaluation of AI agents.
Drive adoption of best practices in AI privacy, security, and compliance.
Optimize platform performance, scalability, and cost-efficiency using cloud-native technologies and distributed systems.

Bachelorʼs or Masterʼs degree in Computer Science, Machine Learning, Data Science, or related field.
Extensive experience designing and building scalable AI/ML platforms or infrastructure in a production environment.
Proven track record of applying LLMs and AI models to real-world product features and user-facing solutions.
Deep expertise in backend engineering, distributed systems, and cloud-native technologies (e.g., Kubernetes, Docker, AWS/GCP).
Proficiency in agentic and orchestration frameworks (e.g., Langchain, CrewAI, pydantic-ai, LangGraph, Airflow, Kubeflow, Ray, or similar).
Strong programming skills in Python, Go, TypeScript or similar languages and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
Experience with MLOps best practices, including model deployment, monitoring, logging, and automated evaluation.
Demonstrated ability to address AI privacy and security challenges, and compliance with data protection regulations.
Familiarity with RAG and its integration into AI-driven applications.
Excellent collaboration and communication skills, with cross-functional teamwork experience.
Passion for staying at the forefront of AI infrastructure and applying new technologies to solve real-world problems at scale.