About the position
Join the dynamic team at Kaseya as a Staff Software Engineer in AI Platform Engineering. Build the intelligent core of our AI platform by engineering autonomous agents that perceive, reason, and learn in complex environments. You’ll pioneer agentic design using LLMs, knowledge graphs, and reasoning engines to create adaptive, context-aware systems that operate in real-world conditions.
Responsibilities
- Design and implement autonomous AI agents capable of perception, reasoning, and learning in complex, real-world environments
- Develop and optimize agentic architectures using LLMs, vector databases, and symbolic reasoning engines
- Apply advanced RAG techniques, prompt engineering, and fine-tuning to enhance agent performance
- Integrate knowledge graphs and multi-modal capabilities to enable contextual and adaptive agent behavior
- Build robust evaluation frameworks, A/B testing pipelines, and model versioning systems
- Collaborate with platform, data, and product teams to ensure seamless deployment and continuous improvement of intelligent agents
Requirements
- 5-7+ years of experience in applied AI/ML, with a strong focus on building intelligent systems and autonomous agents
- Deep expertise in Python and/or TypeScript
- Hands-on experience with multiple LLMs (e.g., GPT, Llama, Claude, Gemini), including fine-tuning, advanced RAG techniques, and prompt engineering
- Proficiency with agent frameworks such as LangChain, LangGraph, AutoGen, or CrewAI
- Experience developing agent planning and reasoning algorithms (e.g., Tree of Thoughts, ReAct, Monte Carlo Tree Search)
- Strong background in model evaluation, A/B testing, and versioning for AI models
- Familiarity with vector databases (e.g., Pinecone, Milvus, Chroma)
- Experience with knowledge graphs (e.g., Neo4j, RDF) and symbolic AI approaches
- Understanding of multi-modal LLM capabilities and responsible AI practices
- Experience with explainable AI (XAI) and responsible AI toolkits
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
Nice-to-haves
- PhD in AI, Cognitive Science, or a related discipline
- Certifications in AI/ML (e.g., DeepLearning.AI, Google Cloud ML Engineer)
- Contributions to open-source AI projects
- Experience with causal inference and its application in agent decision-making
- Familiarity with formal methods for agent behavior verification
- Background in human-AI interaction design and UX for AI-powered applications
- Experience developing agents for domains like cybersecurity or IT operations automation
- Knowledge of neuro-symbolic AI and reinforcement learning from human feedback (RLHF) or direct preference optimization (DPO)
