MLOps & AI Platform Engineer

We’re working to reshape the way manufacturing runs by putting advanced AI solutions into practice — improving productivity, optimizing the way work gets done, and driving efficiency at scale. 

As an MLOps & AI Platform Engineer, you’ll build the backbone that makes AI in production possible. You’ll design and operate the infrastructure, tools, and workflows that enable data scientists and ML engineers to deliver reliable, scalable, and automated AI solutions — from classical ML to LLMs and generative AI. 

This isn’t just about keeping systems running. You’ll drive automation, standardization, and monitoring practices that ensure AI becomes a trusted and sustainable part of manufacturing operations. 

Your Mission 

• Engineer ML & AI Platforms – design and maintain scalable infrastructure that supports the entire ML lifecycle, including training, fine-tuning, and deploying both traditional models and large-scale LLMs. 
• Enable Automation – implement CI/CD pipelines, container orchestration, and workflow automation for seamless deployments across ML and GenAI workloads. 
• Standardize ML Workflows – establish best practices for reproducibility, versioning, model packaging, and deployment across hybrid environments. 
• Deploy & Optimize LLMs – manage GPU clusters, inference servers, and vector databases; optimize performance (throughput, latency, token usage) for generative AI applications. 
• Monitor & Govern – develop observability for ML and GenAI systems (drift, token usage, hallucinations, bias, GPU utilization) while ensuring reliability, security, and compliance. 
• Collaborate Across Teams – work closely with data scientists, AI engineers, and IT to ensure platforms are easy to use, robust, and future-ready. 
• Explore Emerging Tech – evaluate and integrate new MLOps and GenAI frameworks (MLflow, Ray, vLLM, Hugging Face TGI, Triton, orchestration agents) to keep the platform state of the art. 

• Proven experience in MLOps, Data Engineering, or DevOps with focus on ML and AI platforms. 
• Strong skills in Python, containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines (GitHub Actions, Azure DevOps, Jenkins). 
• Hands-on experience with MLflow or equivalent tools for model tracking, packaging, and deployment. 
• Familiarity with cloud data platforms (Azure Databricks, or similar) and distributed computing frameworks. 
• Knowledge of GPU management and inference optimization for LLMs/GenAI workloads (quantization, batching, caching). 
• Understanding of observability practices for ML/GenAI systems, including monitoring, logging, and tracing. 
• Solid grasp of data security, governance, and compliance in enterprise environments. 
• Experience enabling and training AI/ML teams to use platforms effectively, building internal documentation and knowledge resources. 
• Proven ability to balance innovation with operational stability in enterprise environments. 
• A collaborative mindset with eagerness to support data scientists and AI engineers in bringing solutions into production. 
• Strong problem-solving mindset, with ability to translate business needs into data-driven solutions. 

Why Join Us? 

• Impact at Scale – You’ll build the platform that powers AI adoption — from predictive models to generative AI — across a global manufacturing environment. 
• Advanced Tech Playground – Work hands-on with high-performance computing, GPU clusters, and cutting-edge MLOps/GenAI frameworks. 
• Global Collaboration – Be part of an international network of AI, data, and infrastructure experts driving digitalization. 
• Career Growth – Expand your expertise in MLOps, DevOps, and GenAI platforms through challenging projects and global exposure. 
• Innovation-Driven Culture – Work in an agile setup where experimentation and learning are encouraged. 
• Future-Proof Projects – Contribute to building sustainable, data-driven factories of the future. 
• Shape the Future of Manufacturing – play a direct role in redefining how factories operate, using AI to power the next generation of smart, sustainable production.