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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # SAP AI Business Services Consultant - **Company:** OpenKyber LLC - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, DevOps, Python (Programming Language), Machine Learning, NumPy, Tensorflow, SAP (Applications), Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Generative AI, Cloudformation, Pandas, Containerization, Scikit Learn, Kubernetes, Information Technology, Low Latency, Machine Learning Operations, Terraform, Docker - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f69b370f0270ee30 ## About the Role Skill Matrix to be filled by Candidates: Mandatory Skills Years of Experience Year Last Used Rating Out of 10 End-to-End MLOps Automation GenAI Orchestration LLMOps Advanced Model Optimization & Inference Position Details Requirement Role Senior AI/ML & MLOps Engineer Location Remote Type of Hire - Contract/ C2H C2H Salary Range (in USD) Only W2 Job Description Role Overview We are looking for a seasoned AI/ML & MLOps Engineer to lead the development, deployment, and scaling of our machine learning initiatives. You will bridge the gap between data science and production engineering, ensuring our models ranging from traditional predictive analytics to cutting-edge Generative AI are robust, scalable, and high-performing. The ideal candidate doesn't just build models in a vacuum but builds the automated "foundries" that keep them running., * Experience: 4+ years of hands-on experience in ML Engineering or MLOps roles. * Core Stack: Expert-level proficiency in Python and standard ML libraries (Scikit-learn, Pandas, NumPy). * Deep Learning: Strong experience with Transformers , CNNs, or RNNs. * DevOps for ML: Mastery of containerization (Docker) and orchestration (K8s). * Experience with Infrastructure as Code (Terraform/CloudFormation) is a major plus. * GenAI Tools: Familiarity with LangChain, LlamaIndex, or Vector Databases (Pinecone, Milvus, Weaviate). * Education: B.S./M.S. in Computer Science, Mathematics, or a related quantitative field. ## Description * Model Development: Design, train, and optimize ML models using frameworks like PyTorch or TensorFlow . * GenAI Implementation: Lead the integration of LLMs, including fine-tuning, prompt engineering, and building RAG (Retrieval-Augmented Generation) pipelines. * Infrastructure & Orchestration: Architect and maintain end-to-end ML pipelines (CI/CD for ML) using Docker , Kubernetes , and tools like MLflow or Kubeflow . * Cloud Deployment: Deploy and manage production workloads on cloud platforms ( AWS/Google Cloud Platform/Azure ) with a focus on cost-efficiency and low latency. * Monitoring & Governance: Implement robust monitoring for model drift, data quality, and performance metrics to ensure 24/7 reliability. * Collaboration: Work closely with Data Scientists to productize research and with DevOps to align with enterprise security and infrastructure standards. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Data Science on Software Data](https://www.wearedevelopers.com/videos/162-data-science-on-software-data) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)