Top Artificial Intelligence Jobs in Istanbul

The AI Operations Manager will oversee the operational management of AI tools and platforms, ensuring reliability and technical standards while driving improvements and addressing operational challenges.

Lead the design and deployment of scalable machine learning models, collaborate with product teams, enhance AI-driven features, and mentor team members.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Lead a small engineering team building and operating the end-to-end AI product platform: model serving, GPU scheduling, inference pipelines, retrieval/embedding systems, monitoring, and CI/CD. Balance hands-on coding (~50%) with technical leadership, architecture, cross-functional collaboration, hiring and coaching, and driving reliability, cost-efficiency, and AI product quality.

Onboard and support client projects onto Nebius' Azure AI platform using runbooks and golden paths. Adapt Terraform modules and CI/CD pipelines, support ML workload deployment and lifecycle management, capture delivery gaps, and provide structured feedback to Platform Engineering while maintaining security and governance.

Lead and build the AI foundation for an AI-native vulnerability management platform. Own AI strategy, evaluation frameworks for non-deterministic agents, fine-tuning and model-routing experiments, prototype and ship agentic features, and lead a small hands-on AI team to productionize breakthroughs in generative AI for cybersecurity.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Build reusable AI developer tools, RAG knowledge systems, and AI-assisted SDLC workflows. Integrate coding assistants and design-to-code tooling, define secure observable patterns, and own reliability, usage, and cost for internal AI platforms.

Own and improve the quality of AI-powered customer experiences by building evaluation frameworks, running experiments on prompts and model settings, reducing AI costs, diagnosing failure modes, and translating findings into prioritized fixes that drive activation, conversion, retention, and satisfaction.

Lead design, development, testing, and App Store submission of production-scale iOS features in Swift. Collaborate cross-functionally, enforce code quality via reviews and refactoring, maintain technical documentation, and adopt AI-assisted tooling to improve productivity across large, user-facing modules.

Own operational quality, reliability, and standards for Storyteller's AI tooling. Configure, monitor, and improve AI platforms, permissions, integrations, and workflows; identify friction, drive durable solutions, and provide leadership visibility on platform health.

Build and test AI-agent features and internal agent frameworks for a proactive cybersecurity platform. Implement and integrate generative AI systems (agents and RAG), stay current with AI research, and apply innovations to product development and evaluation within a security-focused environment.

Design, build, evaluate, and deploy LLM-driven features and AI systems. Collaborate with engineering and product teams to implement RAG, monitoring, CI/CD, and scalable cloud ML deployments while continuously improving model performance.

Plan, create, publish, and repurpose SEO-focused content across channels; manage marketing calendar; monitor campaign and SEO performance; and design AI-powered marketing workflows, automation, and reusable prompts. Research and implement AI tools, review AI-generated content for accuracy and brand fit, and recommend data-driven improvements.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

The AI Data Expert will evaluate and annotate AI-generated content, ensuring cultural relevance and quality through detailed feedback and analysis. Responsibilities include generating original content, performing quality checks, and adhering to evaluation guidelines.

Review 3D CAD models and engineering drawings for manufacturability, GD&T correctness, and tolerance stack-ups; recommend DFM improvements; develop realistic design/manufacturing scenarios; explain engineering mechanics and tribology; and provide structured feedback to improve AI-generated engineering content.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Build and own end-to-end features for Chainguard's AI-enabled CI/CD and supply-chain security products. Design, implement, and iterate on integrations like hardened GitHub Actions, container tooling, and agentic AI features. Drive architectural decisions, collaborate with Product and front-end teams, and ship secure, production-ready developer tools.

Lead design and implementation of CI/CD and AI-related product features, owning end-to-end delivery. Build and secure CI/CD systems, work with containers, registries, and Kubernetes, extend agentic AI into customer experiences, guide architecture, and collaborate closely with Product and front-end teams.

Build and ship secure, production-ready CI/CD and AI-integrated features end-to-end. Design, implement, and iterate on products (e.g., hardened GitHub Actions, agent features), work closely with Product and front-end teams, make architectural decisions, and extend Chainguard's agentic AI capabilities into customer-facing experiences.

Lead design and build a production-grade generative AI media platform: architect GPU-backed pipelines (ComfyUI/diffusers), train LoRAs, implement control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, create one-click tools and presets for non-technical teams, and own the technical roadmap with design stakeholders.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.