科技職缺
職缺列表
Applied AI Engineer
Build and ship AI agents and production-grade LLM systems at Windward in Tel Aviv, embedding with internal users to design intelligent workflows, integrate with data and APIs, and own architecture, evals, guardrails, and deployment end to end.
AI Engineer
Build production-ready GenAI solutions and multi-agent AI workflows as an AI Engineer, developing Python backends, React frontends, RAG pipelines, and integrating with Azure services. Responsibilities span MLOps/LLMOps, observability, governance, and performance optimization.
Python AI Engineer
Build AI-powered products end-to-end with Python, designing LLM workflows, AI agents, and multimodal solutions, plus analytics platforms that fuse geospatial, IoT, and image data into production dashboards and automation tools.
Senior Artificial Intelligence/Machine Learning Engineer
Builds LLM-powered agents and workflows with production guardrails, instruments cost and quality telemetry, integrates with data sources, and writes and runs LLM evaluations in a senior AI/ML engineering role.
GenAI Engineer
Build RAG pipelines and agentic/multi-agent AI systems while developing Python applications for generative AI. Work spans embeddings, indexing, chunking, reranking, GPU optimization, and integrating AI features into broader systems.
Experienced – Agentic GenAI & Hyper-Automation Developer
Agentic AI and hyper-automation developer at Deloitte building LLM and GenAI applications, RPA/OCR automations, and Power BI/Qlik dashboards to streamline client business processes.
Sr. AI/ML Engineer
Build modular AI agent architectures, RAG pipelines, and LLM-tool integrations to automate hardware design workflows at AMD — covering compiler error resolution, EDA output processing, and knowledge graphs for hardware specs.
AI/Machine Learning Engineer
Designs and implements LLM-based agentic workflows, integrates AI systems with APIs and enterprise data stores, and ensures production reliability and security/governance compliance for mission platforms.
Architect – Agentic AI Engineering
Architect for agentic AI systems: designs Python backend services, builds multi-step AI agent workflows with LLMs, deploys on AWS/Azure, and refines AI prototypes into scalable production systems with prompt engineering, observability, and documented APIs.
AI Data Engineer - Early Career
Early-career AI Data Engineer at DarioHealth building dashboards, KPIs, and ETL/ELT pipelines while contributing to AI-enabled automation. Day-to-day work involves writing Python and SQL, maintaining data models and warehouses, and ensuring data quality and reliability.
AI Engineer
AI Engineer building LLM-powered agents and custom MCP servers in production at Noma Security. Core work spans backend services, REST API integrations, evaluation frameworks, authentication/secrets management, and hardening the MCP gateway ecosystem with reliability and observability.
AI Architect
Design and architect TTEC's AI platform and agent infrastructure, including MCP server integrations, governance standards, and human-in-the-loop workflows.
AI Engineer bei SCOUTASTIC (all genders)
An AI Engineer building experiments, prototypes, and production AI features by integrating LLMs, embeddings, and AI models through data pipelines, owning end-to-end delivery from experiment to production.
Software Engineer, Search Platforms, GenAI Content
Software Engineer role focused on building AI-generated content features and signals for Google's Search Platforms, including applied research on GenAI techniques, multimodality content understanding, and agentic AI evaluation.
Software Engineer III, Generative AI, Core
Software Engineer III role on Google's Generative AI Core team, building product and system code for generative AI. Day-to-day work includes writing and reviewing code, debugging production issues, participating in design reviews, and maintaining documentation.
Software Engineer III, Google Cloud, Google Threat Intelligence
Software Engineer III on Google's Cloud Threat Intelligence team in Málaga, building AI agents and LLM-based security use cases end-to-end—from design and development through deployment and monitoring.