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Senior AI and Machine Learning Platform Engineer
HíbridoTiempo completoAI EngineeringCommercial Real Estate
Join an enterprise data science team building and operating production-grade machine learning and LLM systems. Own scalable model serving, MLOps and LLMOps standards, evaluation, observability, experimentation environments, and the cloud foundations supporting AI applications.
Responsabilidades
- Serve open-weight LLMs at scale using GPU capacity management, autoscaling, batching, quantization, caching, and cost optimization
- Set MLOps and LLMOps standards covering experiment tracking, model and prompt registries, infrastructure as code, and release practices
- Build evaluation, monitoring, regression testing, and cost-tracking systems for non-deterministic LLM pipelines
- Develop scalable, reproducible, and safe development and pre-production environments for large-scale AI experimentation
- Improve CI/CD, engineering, and cloud foundations for machine learning and LLM pipelines
- Design and productionize agentic AI services, multi-agent systems, and visual interfaces
- Partner with data scientists on agents, RAG, fine-tuning, open-weight model hosting, and related generative AI technologies
Requisitos
- At least 5 years of experience in MLOps, machine learning, artificial intelligence, data engineering, or software engineering
- Experience deploying and operating production systems and collaborating with data scientists or researchers
- Hands-on experience with LLMs, vector databases, RAG, MCP, agent platforms, and open-weight models
- Experience serving AI models at scale, including orchestration, architecture, caching, monitoring, latency, throughput, and cost management
- Fluency with MLOps and LLMOps practices such as experiment tracking, model and prompt registries, and production monitoring
- Deep understanding of LLM architectures, including mixture-of-experts, attention variants, tokenization, quantization, KV caching, and batching
- Bachelor's degree in computer science, mathematics, another quantitative field, or equivalent experience
Se valora
- Practical experience with distributed training and inference parallelism, including FSDP and data, tensor, or pipeline parallelism
- MSc in computer science, mathematics, or another quantitative field
- Experience in data science or applied research
- Experience building agentic systems
- Strong data engineering experience across cloud and DevOps
- Experience with AWS, Azure, or GCP; Databricks or Snowflake; Spark; infrastructure as code; CI/CD; and scheduled data pipelines
- Entrepreneurial interest in emerging AI technologies
Beneficios
- Hybrid work from a Tel Aviv headquarters
- Competitive compensation and benefits
- Private health insurance
- On-site gym
- Team breakfasts and a collaborative work environment