Top Artificial Intelligence Jobs in Izmir

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

Lead global marketing and corporate communications to define Multiverse's narrative, positioning, and GTM for AI/quantum products. Partner with the CEO, build and scale the marketing & communications team, own product messaging, PR, executive positioning, agency relationships, and measure impact on awareness and enterprise pipeline.

3–6 month internship supporting risk operations: monitor KRIs and case trends, assist investigations and case analysis, escalate findings, and help optimize AI/LLM tools, automation, and alerting workflows.

Own partnership sourcing, negotiation, and deal execution to drive adoption of Walrus across AI and Web3 ecosystems. Build relationships with AI builders, data vendors, compute networks, and web3 partners; lead contract negotiations; and manage cross-functional integration from signing through execution.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

As a Sales Engineer, you'll demonstrate Camunda's value, support product evaluations, conduct demos, and collaborate on sales strategies to convert prospects.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

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 and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

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.

Own vision, strategy, and roadmap for AI and platform capabilities in a cloud-native data protection product. Define AI-driven features (anomaly detection, classification, risk scoring), platform services (APIs, data pipelines, extensibility), and end-to-end user experiences (policies, RBAC, onboarding, reporting). Partner with engineering, design, security, and go-to-market to prioritize, launch, and iterate on enterprise-grade, explainable AI features and measure success with data-driven metrics.

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.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

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.