Top Artificial Intelligence Jobs in Izmir

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

Perform human review and annotation of AI outputs across text, audio, and video. Generate or assess content for cultural appropriateness, accuracy, and clarity; provide structured annotations and feedback; follow evaluation guidelines and participate in calibration and QA.

Create short- and long-form AI-generated educational and entertainment content for Instagram, TikTok, and YouTube. Rapidly test content hypotheses, analyze performance, research trends and competitors, and iterate on hooks, storytelling, and editing. Collaborate with the product team and continually experiment with AI image/video generation and editing tools.

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.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.

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 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.

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.

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.

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.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Lead applied research and system design for agent-native search: build multi-stage retrieval, ranking, and grounding methods for LLM-driven, iterative agent workflows; deploy high-throughput, low-latency production systems; define evaluation metrics; run experiments and mentor engineers.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

Join a client-facing squad as a hands-on Senior Full-Stack Engineer working across Kotlin backends and React/TypeScript frontends. Apply AI-native delivery practices and agent/harness engineering to accelerate delivery, unblock the team, and share patterns across the organisation while using AWS and cloud data tooling.

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.

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.

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.