AI/ML Ops Engineer

As an AI/ML Engineer at Automation Anywhere, you will be part of the Cloud Operations (CloudOps) team, responsible for designing, developing, and deploying AI/ML models and automation solutions to optimize cloud infrastructure management and operational efficiency. **Primary Responsibilities:** - Design, develop, and deploy machine learning models and AI algorithms for automating cloud infrastructure tasks like monitoring, fault detection, and predictive maintenance. - Collaborate with Cloud Engineers, ML Engineers, Data Scientists, and DevOps teams to integrate AI/ML solutions into cloud orchestration and management platforms. - Build scalable data pipelines and workflows using cloud-native services (preferably AWS, GCP) for real-time and batch ML model training and inference. - Analyze large volumes of cloud telemetry data to extract actionable insights using statistical methods and ML techniques. - Implement anomaly detection, capacity forecasting, resource optimization, and automated remediation solutions. - Develop APIs and microservices to expose AI/ML capabilities for CloudOps automation. - Ensure ML models comply with privacy and governance standards in collaboration with SecOps. - Optimize existing AI/ML workflows for cost, performance, and accuracy. - Stay updated with the latest trends in AI/ML, cloud computing, and infrastructure automation. **Skills & Requirements:** - 6+ years of professional experience in AI/ML engineering, preferably in cloud infrastructure or operations environments. - Proficiency in Python (with relevant libraries like pandas and numpy) or R, or similar programming languages used in AI/ML development. - Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or similar. - Familiarity with MCP Server, AI Agents, and A2A communication, as well as Large Language Model (LLM) operations. - Expertise in cloud platforms (AWS, Azure, GCP) and their AI/ML services. - Strong understanding of data structures, algorithms, and statistical modeling. - Experience with building and maintaining data pipelines using tools like Apache Spark, Kafka, Airflow, or cloud-native alternatives. - Knowledge of containerization and orchestration (Docker, Kubernetes) for deploying ML models in production. - Familiarity with infrastructure monitoring tools (Prometheus, Grafana, ELK Stack) and cloud management platforms. - Experience with CI/CD pipelines and automation tools in cloud environments. - Strong problem-solving skills and ability to work independently and in cross-functional teams. - Excellent communication skills for collaborating with technical and non-technical stakeholders. - Understanding of Infrastructure as Code (IaC) tools like Terraform, CloudFormation. - Knowledge of cybersecurity principles related to cloud and AI/ML systems. Please note that all unsolicited resumes submitted to any @automationanywhere.com email address will not be eligible for an agency fee.,