Top Artificial Intelligence Jobs in Turkey

Lead and execute AI-driven consumer product initiatives for search, recommendations, personalization, and conversational experiences. Collaborate with engineering, design, data, and business teams to define requirements, use data and user feedback to refine products, and deliver improvements within the contract period.

The Backend Engineer will develop AI Agents, design scalable systems, integrate ML services, optimize performance, and mentor junior engineers.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Design, build, and maintain end-to-end scalable data pipelines and Snowflake-native warehouses; optimize SQL and performance; implement Azure-based data platform solutions; develop Power BI semantic models; enable AI/ML data workflows; enforce governance, CI/CD, and security; collaborate with stakeholders and mentor engineers.

Generate AI-driven video creatives from briefs using tools like HeyGen and Runway, produce multiple variations, deliver production-ready assets and notes to motion designers, ensure quality control, and research new AI video tools and workflows to scale marketing production.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

The AI-First Full-Stack Engineer will lead full-stack development using Node.js and Next.js, integrating AI workflows and orchestrating Claude agent teams for efficient engineering processes, ensuring compliance with healthcare standards.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Design, build, and deploy agentic AI solutions and multi-agent workflows using LLMs, RAG, vector databases and embeddings. Define technical standards, guide architecture, integrate AI with enterprise systems, and implement evaluation, monitoring, governance and reliability for production-grade AI in regulated banking domains.

Design and build Python-based intelligent agents and multi-agent workflows using Autogen or similar frameworks. Integrate LLMs, develop cloud-native services, create async APIs, implement data ingestion and storage (SQL/NoSQL/vector DBs), ensure scalability, and contribute to orchestration, testing, documentation, and CI/CD.

As a Backend Engineer, you will develop and scale backend features for an AI-driven video production platform, ensuring code quality and collaboration with the team.

Own the full enterprise GTM lifecycle: prospecting, discovery, demos, pilots, negotiations, closing, and expansion. Map creative workflows, design pilots with metrics, run demos/workshops, manage complex multi-stakeholder deals, coordinate contracts/security reviews, and build repeatable GTM processes and feedback loops to inform product and pricing.

Own end-to-end product for a slice of Nebius AI compute platform: define strategy, APIs, and roadmaps; drive cross-team delivery; conduct structured discovery; measure adoption and platform outcomes; engage engineering as a technical peer; and close operational feedback loops to improve reliability and customer experience.

The Senior Product Manager will lead product strategy, market intelligence, and monetization efforts in EdTech, focusing on AI integration. Responsibilities include mentoring teams and driving data-informed decision-making.

The AI Software Engineer will design and build production-grade LLM applications, develop AI systems, and integrate solutions within enterprise applications.

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

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.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

Lead design, development, and deployment of AI and GenAI systems; collaborate with cross-functional teams to translate business needs into scalable solutions; integrate AI modules into software; mentor junior staff; perform data analysis and model validation; and communicate technical results to non-technical stakeholders.

Lead strategic engagements to accelerate adoption of Genesys AI and Experience Orchestration. Translate business goals into AI use cases, drive field execution, support deal strategy, run customer workshops, create proposals and pricing, capture customer insights, and partner cross-functionally to prioritize AI-led opportunities and pilots.

Design and build internal AI productivity tools and LLM-powered applications to augment developer workflows. Integrate AI into the software development lifecycle, apply techniques like prompt engineering, RAG, and agents, partner with engineering teams to identify bottlenecks, and continuously evaluate emerging AI technologies to improve internal platforms and engineering efficiency.

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.

Design, build, and operate production-grade automations that reduce manual work across Operations, Support, Finance, and Tech. Identify high-volume automation opportunities, integrate with existing systems via APIs/webhooks, apply AI/LLMs for classification/routing/summarization, and ensure reliability, observability, and iterative improvement of workflows.

Create and edit short-form vertical videos for social media using AI tools, ensuring quality, character consistency, and appropriate syncing of audio elements.

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.