Top Artificial Intelligence Jobs in Ankara

Lead technical strategy and build scalable AI/ML platform infrastructure on AWS and Kubernetes. Design IaC, CI/CD, observability, security, and cost/capacity practices. Mentor engineers, improve deployment reliability, and enable generative AI workloads, model routing, and operational metrics across multiple environments and regions.

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.

The role involves designing and deploying AI systems for clinical operations, developing models for decision-making, and ensuring AI implementation aligns with data governance standards. Responsibilities include collaborating across teams, enhancing operational efficiency, and advancing AI applications in a regulated environment.

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.

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

The VP of Business Development will drive commercial growth in Europe, focusing on strategic opportunities with renewable energy stakeholders and building relationships to enhance market presence.

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

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.

Lead the LLM team to architect, train, and deploy AI models, optimizing real-time user experiences and creating advanced moderation systems.

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.

The role involves leading cross-functional teams to define, develop, and launch AI-driven products, ensuring alignment with Insider One's vision and managing product lifecycle from ideation to market release.

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.

Lead Full Stack Engineer owns design-to-production of an AI-first repossession and remarketing platform. Shape architecture, build scalable resilient services, integrate LLMs/GenAI for validation and document processing, drive technical decisions, reduce technical debt, operate production systems, and mentor engineers while working in cross-functional agile teams.

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 Product Manager - AI will define AI product direction, collaborate with engineering, and solve complex user problems in payroll and compliance workflows.

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.

As a Principal AI Researcher, you will lead AI research projects, mentor teams, and implement innovative AI technologies for Veracode's products.

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.

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.

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

The Fullstack Engineer will build modern AI-powered web applications using Next.js and Hono.js, focusing on integrating advanced workflows and scalable features. Responsibilities include designing full-stack features, optimizing performance, and collaborating with team members to deliver high-quality product features.

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.