Top Artificial Intelligence Jobs in Ankara

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.

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.

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.

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

Own end-to-end AI product direction for a D2C subscription product: define vision and roadmap, specify features, drive delivery with engineering and design, run hypothesis-driven experiments (A/B tests), measure quality and business impact with Mixpanel, and scale AI-powered flows to improve retention and growth.

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.

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.

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.

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.

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.

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.

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.

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.

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.