Top Artificial Intelligence Jobs in Istanbul

Evaluate personalized AI responses by designing multi-turn prompts using your own personal context, assessing grounding, integration, and helpfulness, stack-ranking side-by-side model outputs, writing clear rationales, verifying debug info, and maintaining data hygiene. Work full-time as a remote contractor with schedule overlap requirements.

Evaluate personalized AI responses for Gemini by designing multi-turn prompts using personal data, comparing side-by-side responses, assessing grounding, integration, and helpfulness, writing structured rationales, extracting debug info, and maintaining data hygiene.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

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

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 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.

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 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.

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

Support Shopify administration and Flow automations, maintain Google Apps Scripts and API integrations, manage access and SaaS subscriptions, configure Recharge subscription flows, and document processes. Use AI tools (Claude, ChatGPT) and n8n to build automations, test end-to-end, and liaise with vendors and internal teams to keep e-commerce systems running smoothly.

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.

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

Design, implement, and run automated benchmarking and validation pipelines for AI-driven genomic interpretation. Expand regression datasets, execute large-scale validation across curated VCFs, surface accuracy regressions, build automated QA for AI releases, and integrate validation into CI/CD with dashboards and documented failure modes.

Lead evaluation and benchmarking of AI-driven genomic interpretation: own gene/disease/variant mapping logic, define accuracy and release criteria, establish regression standards, partner on automated validation, and guide where deterministic logic supplements models.

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.

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.