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 Leadership Architect multi-LLM systems (e.g., Mixture-of-Experts, LLM routing) for cost-performance optimization Design GPU/TPU-optimized training pipelines (FSDP, DeepSpeed) for billion-parameter models 2. Cloud-Native AI Development Build multi-cloud GenAI platforms (Azure OpenAI, GCP Vertex AI, AWS Bedrock) with unified MLOps Implement enterprise-grade security: VPC peering, private endpoints, data residency compliance 3. Innovation & Strategy Pioneer GenAI use cases: agentic workflows, synthetic data generation, real-time fine-tuning Establish AI governance frameworks: model cards, drift monitoring, red-teaming protocols 4. Cross-Functional Impact Collaborate 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 organization Qualifications:Education:Bachelor’s or Master’s in Computer Science, AI, or equivalent experience (5+ years in ML, 2+ in GenAI)Technical Mastery: Languages: Python Frameworks: Expert-level PyTorch, TensorFlow Extended (TFX), ONNX Runtime Cloud: Certified in Azure AI Engineer Expert and/or GCP Professional ML Engineer GenAI 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 generation Must-Have Experience: Cloud AI Development: Azure: Azure OpenAI, MLOps Pipelines, Cognitive Search GCP: Vertex AI LLM Evaluation, Gemini Multimodal, TPU v5 Pods High-Impact Projects: Automation initiatives with measurable cost savings RAG systems with hybrid search (vector + lexical) and dynamic data hydration AI compliance leadership in regulated industries (e.g., healthcare, finance) Preferred Qualifications: Certifications: Microsoft Certified: Azure AI Engineer Associate Google Cloud Professional Machine Learning Engineer Deployment 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.