Agentic AI Trainer (General AI/Tech Industry)
Locations: Canada, USA
Experience: 2+ years
English: Advanced (C1)
Description:
We are Teiva Systems, a trusted ServiceNow provider with more than 15 years of team experience developing products in collaboration with our clients.
With the guidance of our experts and moderators, we collaborate with clients throughout the entire ideation, business validation, and development process, following a well-defined approach. Teiva Product Incubator is a dedicated space for clients’ business goals and ideas. Our services extend beyond idea generation. We also assist in enhancing and accelerating existing products.
Our clients seek to save costs by swiftly validating ideas, generating additional revenue, expanding their resources, or implementing winning go-to-market strategies. In that case, Teiva Incubator is the ideal partner.
We are hiring the best talents to work in Teiva Systems. We work with enterprise-level companies from the USA, UK, Switzerland, Germany, and other EU countries. Our team locations are Europe, Canada, the USA, and the Philippines.
We are seeking an Agentic AI Trainer to join our team and drive the development of AI systems capable of autonomous decision-making, adaptive learning, and complex problem-solving. This role focuses on training AI agents using advanced machine learning techniques, reinforcement learning, and real-world simulations, ensuring they exhibit goal-oriented behavior in dynamic environments.
The ideal candidate has hands-on experience in training AI agents, fine-tuning large language models (LLMs), and developing frameworks that enhance AI autonomy, adaptability, and automation in enterprise settings. You will collaborate closely with data scientists, machine learning engineers, and product teams to optimize AI performance and deployment.
AI Model Training & Optimization
- Design, implement, and refine training processes for agentic AI models, ensuring continuous learning and adaptation.
- Develop high-quality datasets, interactive environments, and real-world simulations to enhance AI decision-making capabilities.
- Fine-tune LLMs and multimodal AI to support goal-oriented and context-aware behavior.
- Implement reinforcement learning (RL), self-supervised learning (SSL), and adaptive training techniques.
AI Performance Enhancement & Compliance
- Monitor AI behavior, conduct performance analysis, and iteratively refine training processes based on empirical results.
- Ensure AI models align with ethical AI principles, safety protocols, compliance regulations, and industry best practices.
- Utilize human feedback
Enterprise AI & Automation
- Design and develop AI-driven automation frameworks for enterprise environments.
- Train AI agents to handle workflow automation, autonomous decision-making, and self-correcting actions.
- Collaborate with AI engineers and product teams to optimize model deployment and integration into enterprise applications.
- Ensure AI decision-making aligns with business objectives, security requirements, and operational efficiency goals.
Qualifications & Skills
Required:
- Bachelor’s, Master’s, or PhD in AI, Computer Science, Machine Learning, or a related field.
- Hands-on experience with machine learning frameworks (TensorFlow, PyTorch, JAX, etc.).
- Strong expertise in reinforcement learning, deep learning, and agent-based modeling.
- Familiarity with prompt engineering, fine-tuning, and optimizing LLMs.
- Solid understanding of AI safety, interpretability, and ethical AI development.
- Proficiency in Python and AI/ML libraries (NumPy, SciPy, Hugging Face, etc.).
- Experience in training AI models for real-world applications and enterprise use cases.
- Ability to work cross-functionally in a fast-paced AI research and development environment.
Preferred:
- Experience in training AI agents for robotics, gaming, digital assistants, or multi-agent systems.
- Strong background in AI-powered automation, workflow optimization, and RPA.
- Knowledge of enterprise AI integration (ServiceNow, Salesforce, SAP, etc.).
- Contributions to AI/ML research communities, publications, or open-source projects.
- Understanding of AI governance, explainability, and enterprise compliance frameworks.
