Employment Type: Full-Time Salary Range: Up to $1500 Work Schedule:
Time Range: Between 7 AM and 7 PM CST
Working Hours: 9 hours per day (8 working hours 1-hour break)
Days Off: TBD (2 days per week)
Why Join Us
At Teamficient our team spans multiple countries and regions and we stay connected by operating within EST CST and PST time zones.
Work Without Borders: Collaborate daily with experts from around the world and gain international exposure.
Built for Remote: Join a fully remote culture designed for autonomy flexibility and trust.
Diverse Perspectives: Be part of a multicultural team where different backgrounds are our greatest strength.
Grow Globally: Expand your career on a global stage learning how business works across different cultures and continents.
About the Role
TeamFicient is looking for an experienced AI Developer/Engineer to design build and scale production-grade AI applications that deliver real value to our clients. Youll work at the intersection of applied AI backend engineering and cloud infrastructure building systems that are robust reliable and ready for the real world. If you have a proven track record of shipping LLM and RAG-based systems in production this role is for you.
Core Responsibilities
AI Application Development
Design and build AI-powered applications using LLMs RAG architectures and other applied AI systems
Develop and maintain backend services that support AI platforms and integrate seamlessly with cloud infrastructure
Build and maintain integrations across various applications to extend AI capabilities
Cloud and Infrastructure
Deploy optimize monitor and troubleshoot AI solutions on AWS Azure and/or GCP
Implement containerization and orchestration strategies using Docker and Kubernetes
Ensure AI systems meet performance scalability and reliability standards in production
Architecture and Collaboration
Design scalable AI architectures that translate business requirements into technical solutions
Collaborate with cross-functional teams throughout the full development lifecycle
Contribute to code reviews technical documentation and engineering best practices
Candidate Qualifications
Must-Haves
5 years of professional experience in AI engineering machine learning or software engineering with an AI focus including production deployments and strong proficiency in Python and AI/ML frameworks with a degree in Computer Science Engineering or equivalent practical experience
2-3 years of hands-on experience with LLMs and RAG architectures in production environments including vector databases
3 years of experience with cloud platforms AWS Azure or GCP
2 years of experience with Docker and Kubernetes for containerization and orchestration
Good to Haves
Familiarity with MLOps practices and tools for model deployment and monitoring
Knowledge of additional programming languages such as Go Java or JavaScript/TypeScript
Experience with CI/CD pipelines and infrastructure as code (Terraform CloudFormation)
Contributions to open-source AI/ML projects or active participation in the AI community
Masters degree or PhD in Computer Science Machine Learning AI or a related field
Required Skills:
Must-Haves - 5 years of professional experience in AI engineering machine learning or software engineering with an AI focus including production deployments and strong proficiency in Python and AI/ML frameworks - 2-3 years of hands-on experience with LLMs and RAG architectures in production environments including vector databases - 3 years of experience with cloud platforms AWS Azure or GCP - 2 years of experience with Docker and Kubernetes for containerization and orchestration Good to Haves - Familiarity with MLOps practices and tools for model deployment and monitoring - Knowledge of additional programming languages such as Go Java or JavaScript/TypeScript - Experience with CI/CD pipelines and infrastructure as code (Terraform CloudFormation) - Contributions to open-source AI/ML projects or active participation in the AI community - Masters degree or PhD in Computer Science Machine Learning AI or a related field
Required Education:
Degree in Computer Science Engineering or equivalent practical experience
This is a remote position.AI Developer/Engineer Company: TeamFicient Location: RemoteEmployment Type: Full-Time Salary Range: Up to $1500Work Schedule: Time Range: Between 7 AM and 7 PM CST Working Hours: 9 hours per day (8 working hours 1-hour break) Days Off: TBD (2 days per week) Why Join ...
This is a remote position.
AI Developer/Engineer
Company: TeamFicient Location: Remote
Employment Type: Full-Time Salary Range: Up to $1500 Work Schedule:
Time Range: Between 7 AM and 7 PM CST
Working Hours: 9 hours per day (8 working hours 1-hour break)
Days Off: TBD (2 days per week)
Why Join Us
At Teamficient our team spans multiple countries and regions and we stay connected by operating within EST CST and PST time zones.
Work Without Borders: Collaborate daily with experts from around the world and gain international exposure.
Built for Remote: Join a fully remote culture designed for autonomy flexibility and trust.
Diverse Perspectives: Be part of a multicultural team where different backgrounds are our greatest strength.
Grow Globally: Expand your career on a global stage learning how business works across different cultures and continents.
About the Role
TeamFicient is looking for an experienced AI Developer/Engineer to design build and scale production-grade AI applications that deliver real value to our clients. Youll work at the intersection of applied AI backend engineering and cloud infrastructure building systems that are robust reliable and ready for the real world. If you have a proven track record of shipping LLM and RAG-based systems in production this role is for you.
Core Responsibilities
AI Application Development
Design and build AI-powered applications using LLMs RAG architectures and other applied AI systems
Develop and maintain backend services that support AI platforms and integrate seamlessly with cloud infrastructure
Build and maintain integrations across various applications to extend AI capabilities
Cloud and Infrastructure
Deploy optimize monitor and troubleshoot AI solutions on AWS Azure and/or GCP
Implement containerization and orchestration strategies using Docker and Kubernetes
Ensure AI systems meet performance scalability and reliability standards in production
Architecture and Collaboration
Design scalable AI architectures that translate business requirements into technical solutions
Collaborate with cross-functional teams throughout the full development lifecycle
Contribute to code reviews technical documentation and engineering best practices
Candidate Qualifications
Must-Haves
5 years of professional experience in AI engineering machine learning or software engineering with an AI focus including production deployments and strong proficiency in Python and AI/ML frameworks with a degree in Computer Science Engineering or equivalent practical experience
2-3 years of hands-on experience with LLMs and RAG architectures in production environments including vector databases
3 years of experience with cloud platforms AWS Azure or GCP
2 years of experience with Docker and Kubernetes for containerization and orchestration
Good to Haves
Familiarity with MLOps practices and tools for model deployment and monitoring
Knowledge of additional programming languages such as Go Java or JavaScript/TypeScript
Experience with CI/CD pipelines and infrastructure as code (Terraform CloudFormation)
Contributions to open-source AI/ML projects or active participation in the AI community
Masters degree or PhD in Computer Science Machine Learning AI or a related field
Required Skills:
Must-Haves - 5 years of professional experience in AI engineering machine learning or software engineering with an AI focus including production deployments and strong proficiency in Python and AI/ML frameworks - 2-3 years of hands-on experience with LLMs and RAG architectures in production environments including vector databases - 3 years of experience with cloud platforms AWS Azure or GCP - 2 years of experience with Docker and Kubernetes for containerization and orchestration Good to Haves - Familiarity with MLOps practices and tools for model deployment and monitoring - Knowledge of additional programming languages such as Go Java or JavaScript/TypeScript - Experience with CI/CD pipelines and infrastructure as code (Terraform CloudFormation) - Contributions to open-source AI/ML projects or active participation in the AI community - Masters degree or PhD in Computer Science Machine Learning AI or a related field
Required Education:
Degree in Computer Science Engineering or equivalent practical experience