Bitovi

Applied AI Engineer - Consultant

Bitovi

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Job Level

Mid-LevelSenior

Tech Stack

AWSCloudDockerKubernetesOpen SourcePythonTypeScript

About the role

  • Build production-ready Intelligent Automations and Agentic AI solutions for enterprise clients
  • Guide clients to build maintainable, scalable, tested, documented software
  • Collaborate with clients and stakeholders to determine requirements and best practices
  • Communicate effectively as a consultant and help enterprise clients make informed decisions
  • Contribute to design, development, and delivery of AI tooling and automation platforms
  • Lead or contribute to projects with a focus on TypeScript/Python, LangChain, and AI models
  • Engage with internal teams and contribute to the Bitovi ecosystem including open source contributions

Requirements

  • Passion for using and building AI tools and solutions
  • Proven ability to deliver real-world solutions and platforms
  • Ability to lead and be an integral part of a team
  • Strongly motivated and able to work autonomously
  • Accepting of suggestions and constructive feedback
  • Driven to continuously learn and improve
  • Enjoys solving complex problems
  • Developing automation tools at the enterprise level
  • Building workflow automation using tools such as n8n or Rivet
  • Strong working knowledge of TypeScript or Python, and LangChain
  • Practical use of multiple AI models and content modalities
  • Leveraging vector databases and advanced RAG techniques
  • Utilizing tool calling and other advanced model features
  • Working directly with clients or other stakeholders
  • Some exposure to cloud deployments and management, including AWS, Docker, and Kubernetes
  • Deep understanding of CI/CD pipelines, including GitHub actions, TravisCI, and CodeBuild
  • Observability techniques, including logging, metrics, and tracing
  • Automated testing, including unit and integration testing, performance testing, and benchmarking
  • Fine-tuning models or building custom models
  • Deploying open-source models to production environments
  • Designing and building robust feedback systems for reinforcement learning