Top Artificial Intelligence Jobs in Istanbul

Lead planning, design, delivery and promotion of a customer-service chatbot management platform. Use AI and big data to improve CS operations (corpus training, SOPs, assistant replies, summaries). Collaborate with engineering, UX, and CS teams. Monitor KPIs and user cases to drive product improvements.

Lead AI-driven growth product strategy to improve acquisition, activation, retention, cross-sell, and churn reduction. Design viral loops and referral programs, use data and user insights to inform features, manage roadmaps, run experiments, and collaborate with marketing, operations, and engineering to optimize user funnels and drive scalable organic growth.

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 development and scaling of AI-powered clinical applications using LLMs. Gather user and industry requirements, write product specifications, prioritize roadmaps, coordinate sprints and releases, validate quality with engineering and QA, and design generative AI capabilities to streamline pharmaceutical workflows and regulatory documentation.

Owner of SDK quality who uses AI coding agents to design, build and run unit, component and integration tests across mobile SDKs. Investigate failures, improve testability, integrate tests into CI, and turn repeat work into reusable AI-assisted workflows while working directly with engineers and product owners.

Early-career full-time program offering training, mentorship, and hands-on work within Binance's blockchain ecosystem. Participants develop practical skills in Web3, collaborate across teams, and contribute to product and engineering projects while receiving tailored curriculum and 1:1 mentorship.

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.

Build and operate the internal AI platform used by 600+ developers: secure AI coding environments, AI-assisted CI and agentic AWS workflows. Own products end-to-end, improve developer experience, ensure reliability/observability/cost-awareness, participate in on-call, and capture reusable patterns and templates for broad adoption.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Partner with Account Executives to present Arize's GenAI observability platform, lead technical demos and discovery, design and run PoCs, advise on GenAI/LLM best practices, handle technical objections, and build relationships with technical and business stakeholders across prospective customers.

Lead delivery and scaling of generative AI data programs across multiple projects: capacity planning, vendor management, quality consistency, process automation, financial control, risk mitigation, client reporting, and team enablement.

Lead and scale portfolios of generative AI data programs (collection, labeling, evaluation). Manage capacity planning, vendor SLAs, quality consistency, process automation, budgets, risk, stakeholder reporting, and coach PMs/coordinators while ensuring data security and compliance.

Assess client engineering workflows, design and build agentic tooling and processes, instrument solutions for cost and business outcomes, run demos and enablement sessions, support teams to independent operation, and contribute technical leadership to presales when needed.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Lead analysis and improvement of conversational AI systems by designing prompt strategies, optimizing RAG and retrieval, evaluating LLMs, defining AI quality metrics, running experiments, and collaborating with engineers to deploy production-scale improvements.

Design, build, and productionize AI features including conversational and voice AI. Integrate LLMs and AI APIs, implement RAG and agent workflows, troubleshoot production issues, optimize performance and costs, and collaborate with product and AI science teams to deliver scalable, reliable AI systems.

Lead end-to-end architecture for enterprise data, analytics, and AI in a Microsoft-first environment. Translate business needs into secure, scalable designs, prototype and guide delivery teams, ensure governance, security, and production readiness, and enable enterprise AI (Azure OpenAI, RAG) and BI (Power BI) solutions.

Internship role to design and build LLM-powered recommendation and agentic AI systems for financial and Web3 scenarios. Tasks include candidate generation, ranking, tool routing, post-training (SFT/RL), datasets and evaluation pipelines, prototyping agent workflows, and integrating prototypes into production with cross-functional teams.

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.

Own and scale the connector and integrations ecosystem powering AI features. Prioritise, specify, and ship MCP servers, third-party APIs, and app integrations; define evaluation and monitoring frameworks; run experiments and build dashboards; collaborate closely with AI engineers, growth, data, and billing to measure and improve adoption, reliability, latency, cost, retention, and engagement.

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Early-career intern will support AI projects for compliance and risk (KYC, KYB, EDD). Tasks include testing and optimizing LLM/Agent workflows, reviewing prompts and model outputs, documenting findings and edge cases, supporting small-scale implementations, and performing operational analysis to improve processes.

Internship role supporting development, optimization, and deployment of computer vision models. Write Python code with PyTorch/TensorFlow, integrate vision solutions, run experiments, participate in code reviews and agile processes, and collaborate across engineering, product, and data teams to build prototypes and production features.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.