Applied AI Engineer
- תל אביב
- משרה קבועה
- משרה מלאה
- Design and deliver robust backend services and orchestration pipelines, such as Natural Language Query, that power AI features across the Sisense platform.
- Operationalize LLM-based and ML-driven logic, including prompt engineering and chaining into production-grade APIs.
- Own orchestration pipelines and agentic workflows, including multi-agent communication and modular tool execution.
- Integrate with platform infrastructure and AI services, including LLM gateways, vector databases, and centralized configuration tools.
- Collaborate cross-functionally with Software Engineers, Data Scientists, Infra Engineers, and Product Managers to ensure seamless handoff and transformation of experimental logic into reliable services.
- Drive internal tooling and reusable components to accelerate AI feature delivery across teams.
- Ensure high performance, scalability, and resilience of deployed AI-powered services.
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience building production AI systems.
- 4+ years building production systems that integrate ML, LLMs, or AI workflows
- Strong proficiency in backend technologies such as Node.js, Python, or Go.
- Proven track record building APIs, orchestration systems, and pipelines supporting ML or LLM-based features.
- Familiarity with ai development frameworks such as LangChain, LangGraph, LangFuse, MLflow, or similar.
- Comfortable with cloud platforms (e.g., AWS, GCP, Azure) and DevOps practices.
- Understanding of distributed systems and scalable service design.
- Excellent collaboration skills and ability to communicate complex systems to both technical and non-technical stakeholders.
- Experience with agentic systems or multi-agent orchestration in AI workflows.
- Exposure to Knowledge Graphs, RAG (Retrieval-Augmented Generation), or semantic search.
- Understanding of AI infrastructure components such as prompt lifecycle, fallback logic, and feature-level configuration.
- Familiarity with vector databases and AI observability practices.
Mploy