Informacje o stanowisku

technologies-expected :

• Python

• 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.

• 1.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

• 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)

• private medical care
• sharing the costs of professional training & courses
• life insurance
• remote work opportunities
• flexible working time
• fruits
• integration events
• dental care
• corporate library
• video games at work
• coffee / tea
• parking space for employees
• christmas gifts
• employee referral program
• extra leave
• competitive salary
• friendly atmosphere

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