We’re looking for a Senior Generative AI Developer to lead the design and deployment of enterprise-grade GenAI systems. This is a hands-on individual contributor role with opportunities to provide technical guidance to junior developers. You’ll drive innovation in LLM orchestration, multimodal architectures, and scalable AI/ML pipelines—owning the full lifecycle from research to production while ensuring alignment with business goals and ethical AI standards.Key Responsibilities:Technical LeadershipArchitect multi-LLM systems (e.g., Mixture-of-Experts, LLM routing) for cost-performance optimizationDesign GPU/TPU-optimized training pipelines (FSDP, DeepSpeed) for billion-parameter models2. Cloud-Native AI DevelopmentBuild multi-cloud GenAI platforms (Azure OpenAI, GCP Vertex AI, AWS Bedrock) with unified MLOpsImplement enterprise-grade security: VPC peering, private endpoints, data residency compliance3. Innovation & StrategyPioneer GenAI use cases: agentic workflows, synthetic data generation, real-time fine-tuningEstablish AI governance frameworks: model cards, drift monitoring, red-teaming protocols4. Cross-Functional ImpactCollaborate with leadership to define AI roadmaps and ROI metrics (e.g., cost savings via automation)Mentor junior engineers and promote GenAI best practices across the organizationQualifications:Education:Bachelor’s or Master’s in Computer Science, AI, or equivalent experience (5+ years in ML, 2+ in GenAI)Technical Mastery:Languages: PythonFrameworks: Expert-level PyTorch, TensorFlow Extended (TFX), ONNX RuntimeCloud: Certified in Azure AI Engineer Expert and/or GCP Professional ML EngineerGenAI Expertise:Delivered production-grade GenAI systems (e.g., 10k+ QPS chatbots, GitHub Copilot-scale autocomplete)Advanced prompt engineering: self-critique chains, LLM cascades, guardrail-driven generationMust-Have Experience:Cloud AI Development:Azure: Azure OpenAI, MLOps Pipelines, Cognitive SearchGCP: Vertex AI LLM Evaluation, Gemini Multimodal, TPU v5 PodsHigh-Impact Projects:Automation initiatives with measurable cost savingsRAG systems with hybrid search (vector + lexical) and dynamic data hydrationAI compliance leadership in regulated industries (e.g., healthcare, finance)Preferred Qualifications:Certifications:Microsoft Certified: Azure AI Engineer AssociateGoogle Cloud Professional Machine Learning EngineerDeployment Experience:Hybrid/multi-cloud GenAI setups (e.g., training on GCP TPUs, serving via Azure endpoints)What you can expect from us:Flexible work hours (we start between 7:00 and 10:30 am).8-hour work time with a lunch break already included - spend the rest of the day doing what is important to you as intended in the #2h4Family program.Hybrid work model (50/50)Integration events - monthly delicious breakfasts, movie nights, board game nights, outdoor events.Lively and modern office in the City Centre with parking space for employees.A supportive and friendly atmosphere created by passionate people.