Top Artificial Intelligence Jobs in Bursa

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.

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.

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.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.

Contribute to design and delivery of AI-powered platforms and full-stack systems. Build AI-driven features, intelligent automation, and agentic systems; integrate AI into products and workflows; participate in production support, code reviews, and cross-functional collaboration under senior guidance.

Create short-form vertical videos for TikTok, Instagram Reels, and YouTube Shorts, using AI tools and editing techniques to produce serial micro-dramas.

Design, develop, and maintain Python and Go microservices for data ingestion and processing; integrate backend services with BigQuery and cloud data platforms; implement REST APIs; build scalable data processing pipelines using Apache Beam and Dataflow; test and troubleshoot distributed systems; review designs, estimate work, mentor junior developers, and drive performance and reliability improvements.

The Business Development Rep will qualify clients and manage outreach in AI Cloud sales, engaging technical audiences and developing partnerships.

The AI Data Expert will evaluate and annotate AI-generated content, ensuring cultural relevance and quality through detailed feedback and analysis. Responsibilities include generating original content, performing quality checks, and adhering to evaluation guidelines.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Own and scale the connector and integration ecosystem for AI consumer products. Prioritise, specify, onboard, monitor, and maintain MCP servers, third-party APIs, and app integrations. Work closely with AI engineers on prompt systems, structured outputs, agentic workflows, and evaluation. Define success metrics, run experiments/A-B tests, build dashboards, and drive end-to-end product delivery and iteration.

The role involves providing legal guidance on global privacy and AI issues, collaborating with teams to ensure compliance, and developing scalable processes.

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Provide hands-on stabilization of multiple early-stage, often AI-generated codebases: assess, fix bugs, refactor, add missing core functionality (auth, payments), improve performance, implement lightweight testing, and collaborate across teams on fractional hourly engagements.

Own SEO strategy and KPI progress for client accounts, build AI-supported workflows and automations, create repeatable SEO processes, lead client communications and reviews, and drive measurable visibility and business outcomes by combining AI tools and specialist expertise.

Lead full‑stack development of high‑traffic public websites, integrate AI tooling into the SDLC, design scalable backend and performant UIs, maintain cloud infrastructure, CI/CD, monitoring, and testing, and own services under a you‑build‑it‑you‑run‑it philosophy.

Lead end-to-end technical projects for a Voice AI CX platform: scope, plan, manage releases, align product to customer requirements, track metrics, ensure quality, and communicate status to clients and engineering.

Lead QA automation for AI/LLM systems: define validation criteria for non-deterministic models, build automated and performance tests, validate RAG systems, perform API and integration testing, ensure release readiness and trace defects to data/models/requirements.

Lead a cross-functional squad to architect and ship AI-native workflows, integrations, and experimentation infrastructure that scale growth. Build data frameworks, attribution and analytics stacks, run rigorous A/B programs, package repeatable AI enablement toolkits, and collaborate with Engineering to deploy agentic product surfaces.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

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.

Develop and maintain open-source, silicon-optimized AI/ML inference application packages (Inference Snaps) for Linux/Ubuntu Core. Debug, write high-quality code, review upstream contributions, secure and optimize local inference deployments across embedded devices to gateways, and collaborate with a distributed team and stakeholders.

Customer-facing ML/GenAI engineer who advises customers, runs demos and strategic reviews, drives product adoption, partners with sales and engineering, and helps expand accounts by implementing best practices for production ML/LLM systems.