Senior Associate, AI Engineer
PwC
CloudDockerKubernetesMicroservices
Applied AI Engineer - Consultant
Bitovi
full-time
Posted on:
Location: 🇺🇸 United States
Visit company websiteJob 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
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