Job Description
<h3>📋 Description</h3> • Bitovi helps companies create better web applications.
• We are seeking candidates to help build production-ready Intelligent Automations and Agentic AI solutions for our diverse portfolio of enterprise clients.
• We guide our clients to build things the "right way": maintainable, scalable, tested, documented, etc.
• Bitovians are consultants who are knowledgeable and proficient communicators and can help enterprise clients make the right decisions for their applications.
• In addition to client work, you can shape a growing company by applying your talent and passion toward an area of interest.
• Bitovi team members: Speak at conferences, Give trainings, Run meetups, Maintain open source projects, Write articles <h3>🎯 Requirements</h3> • 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