We are looking for a Specialist who will lead the design and deployment of enterprise-grade generative AI systems, driving innovation in LLM orchestration, multimodal architectures, and scalable AI/ML pipelines. Own the full lifecycle from research to production, ensuring alignment with business objectives and ethical  AI standards. This will be a hands-on individual contributor role as well as providing technical guidance to junior developers. 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. Cloud-Native AI Development Build multi-cloud GenAI platforms (Azure OpenAI + GCP Vertex AI + AWS Bedrock) with unified MLOps. Implement enterprise security: VPC peering, private model endpoints, and data residency compliance. Innovation & Strategy Pioneer GenAI use cases: Agentic workflows, AI-driven synthetic data generation, real-time fine-tuning. Establish AI governance frameworks: Model cards, drift monitoring, and red-teaming protocols. Cross-Functional Impact Partner with leadership to define AI roadmaps and ROI metrics (e.g., $ saved via AI-driven automation). Mentor junior engineers and evangelize GenAI best practices across the organization. Qualifications Education: Bachelors/Masters in CS/AI or equivalent industry 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: Shipped production GenAI systems (e.g., 10k+ QPS chatbots, code autocomplete at GitHub Copilot scale). Advanced prompt/response engineering: Self-critique chains, LLM cascades, guardrail-driven generation. Must-Have Experience Cloud AI experience: Azure: Designed solutions with Azure OpenAI, MLOps Pipelines, and Cognitive Search. GCP: Scaled Vertex AI LLM Evaluation, Gemini Multimodal, and TPU v5 Pods. High-Impact Projects: Automation projects to reduce significant $$ costs. Built RAG  systems with hybrid search (vector + lexical) and dynamic data hydration. Led AI compliance for regulated industries (healthcare, finance). Preferred Qualifications Additions Certifications: Azure: Microsoft Certified: Azure AI Engineer Associate. GCP: Google Cloud Professional Machine Learning Engineer. Experience with hybrid/multi-cloud GenAI deployments (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.