Summary: Seeking an MLOps/AIOps/LLMOps/AgentOps Engineer to design, operate, and evolve an AIOps platform, ensuring reliable, scalable, and cost-efficient AI product operations. Highlights: 1. Design and evolve a cutting-edge AIOps platform for AI products 2. Work with modern cloud technologies, including AWS and Azure 3. Collaborate with multidisciplinary Data & AI teams We are looking for a **MLOps / AIOps / LLMOps / AgentOps Engineer** to join a multidisciplinary Data \& AI team. The main mission of this role is to **design, operate, and continuously evolve our AIOps platform**, ensuring that our AI products run in a **reliable, scalable, and cost‑efficient** way. This position is **strongly focused on platform, infrastructure, automation, observability, and operations** rather than on building ML models or AI products themselves. You will work with modern cloud technologies (mainly **AWS**, with some **Azure** exposure) and collaborate closely with **Data Scientists, Data Engineers, and Product teams** to bring AI solutions into production and keep them running smoothly. We are open to candidates with **strong expertise in at least one core area** (e.g. cloud, DevOps, platform engineering, or ML operations) and **solid foundational knowledge in the others**, with motivation to grow across the full AI operations stack. **Key Responsibilities** * **Design, maintain, and evolve the AIOps platform** supporting: + Traditional machine learning models in production + LLM‑based solutions such as **RAG pipelines and AI Agents** + **Speech Analytics** use cases (ASR, conversation analysis, NLP) * **Build and operate ML and LLM pipelines** with a strong focus on: + Reliability, automation, and observability + Model and LLM quality, performance, and drift monitoring + Cloud cost control and optimization * **Implement LLMOps / AgentOps practices**, including: + LLM evaluation and observability + Prompt management, traceability, and specialized logging + Agent integration, orchestration, and lifecycle management * **Ensure continuous operation of AI products**, including: + Alerts, dashboards, SLOs / SLIs + Scalability strategies and basic auto‑remediation mechanisms * **Manage deployments in cloud environments** (AWS / Azure) and container platforms (Docker / Kubernetes) * **Collaborate closely with Data Scientists and Data Engineers** to productionize robust, scalable AI solutions * **Contribute to internal standards, automation, and best practices** across the AI and data ecosystem **Required Skills (Must Have)** * Hands‑on experience in **MLOps, AIOps, or operating ML systems in production** * Solid understanding of **LLMOps and AgentOps concepts** (RAGs, agents, evaluation, monitoring) * Experience working with **AWS and/or Azure** in production environments * Practical knowledge of **containers and Kubernetes** (Docker, basic Helm usage, etc.) * Experience with **CI/CD pipelines** (GitHub Actions, GitLab CI, Azure DevOps, Jenkins, or similar) * Familiarity with **observability and monitoring concepts** (CloudWatch, OpenTelemetry, Prometheus, etc.) * Experience managing infrastructure as code (**Terraform, Bicep, CDK, or similar**) * **Python** experience and familiarity with the ML ecosystem (e.g. scikit‑learn, PyTorch), even if not a Data Scientist * Good understanding of the **ML / LLM lifecycle**, from development to production and monitoring * **Fluent English** to work in an international environment **Nice to Have (Not Required, but Valuable)** * Experience with ML/AI platforms such as **SageMaker, Azure ML, MLflow, Kubeflow** * Exposure to **Speech Analytics technologies** (ASR, diarization, conversational NLP) * Experience with **cloud cost optimization / FinOps**, especially for AI workloads * Experience building or operating **AI agents, copilots, or conversational systems** * Familiarity with **LLM frameworks** (LangChain, LlamaIndex, Semantic Kernel, etc.) * Experience with **workflow and orchestration tools** (Airflow, Argo, Step Functions, Durable Functions) **Professional Skills \& Mindset** * Strong focus on **reliability, automation, and scalability** * Ability to collaborate effectively in **multidisciplinary teams** * Clear communication and documentation‑oriented mindset * **Platform mindset**: building reusable, maintainable, and robust solutions * Proactive, analytical, and continuous‑improvement driven * Strong sense of **ownership and end‑to‑end responsibility** * Motivation to **learn and grow across the AI operations stack** **Technology Environment** * **Cloud**: AWS, Azure * **Orchestration \& Containers**: Kubernetes, Docker * **CI/CD**: GitHub Actions, GitLab CI, Azure DevOps * **Observability**: Prometheus, Grafana, ELK/EFK, OpenTelemetry * **Infrastructure as Code**: Terraform, Bicep, CloudFormation * **AI / ML Tools**: MLflow, Azure ML, SageMaker, LangChain, LlamaIndex, Semantic Kernel * **Primary Language**: Python