Location: India (Remote / Hybrid)
Employment Type: Full-time
Salary Range: $220,000 USD (depending on experience)
Zapeon is hiring an AI Researcher to design, evaluate, and advance applied AI systems used in real-world enterprise workflows.
This role focuses on research-driven development of AI models and agents, with an emphasis on reliability, evaluation, and deployment in production environments rather than purely academic research.
Company Description
Zapeon builds applied AI systems that help organizations operationalize intelligent agents in real business settings.
We work at the intersection of large language models, data pipelines, and human-in-the-loop workflowsturning research into systems that can be deployed, evaluated, and trusted in production. Zapeon focuses on practical AI: models and agents that deliver measurable value, not demos.
What You'll Do
- Research and prototype AI models and agent architectures for enterprise use cases
- Design and run experiments to evaluate model performance, robustness, and failure modes
- Work with large-scale structured and unstructured data, including text and operational data
- Develop evaluation frameworks for AI agents, including benchmarks and test harnesses
- Collaborate closely with engineering and product teams to transition research into production
- Investigate model alignment, safety, bias, and reliability in applied settings
- Document research findings clearly for technical and non-technical stakeholders
- Track and assess relevant academic and industry research for practical applicability
What We're Looking For
- Experience in AI research, applied machine learning, or advanced data science roles
- Strong grounding in machine learning fundamentals (e.g. NLP, deep learning, evaluation methods)
- Proven ability to design experiments and reason rigorously about results
- Strong Python skills and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX)
- Ability to work across research, engineering, and product functions
Bonus Skills
- Experience working with large language models (LLMs) or AI agents
- Background in evaluation, data annotation, or human-in-the-loop systems
- Familiarity with model deployment considerations in production environments
- Research publications, open-source contributions, or technical writing
Success Looks Like
- Research outputs that measurably improve AI system performance and reliability
- Prototypes that successfully transition into deployed systems
- Clear evaluation methodologies that guide product and engineering decisions
- Strong collaboration between research, engineering, and product teams
Interested
Apply on LinkedIn by sending your profile + CV, or message us directly on LinkedIn.
