AI/ML Ops Engineer

Role

- We are seeking a seasoned AI/ML Engineer to join our Cloud Operations (CloudOps) team. The ideal candidate should have strong expertise in designing, developing, and deploying AI/ML models and automation solutions that optimize cloud infrastructure management and operational efficiency.

Primary Responsibilities

- Design, develop, and deploy machine learning models and AI algorithms to automate cloud infrastructure, monitoring, fault detection, and predictive maintenance.

- Collaborate with Cloud Engineers, ML Engineers, Data Scientists, and DevOps teams to 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 (logs, metrics, traces) 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.

- Work with SecOps to ensure ML models comply with privacy, and governance standards.

- 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.

- Strong proficiency in Python (relevant libraries such as 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.

- Hands-on experience with MCP Server, AI Agents, and A2A (Agent-to-Agent) communication

- Experience with Large Language Model (LLM) operations (LLM Ops)

- Expertise in cloud platforms (AWS, Azure, or GCP) and their AI/ML services (e.g., SageMaker, Bedrock, Anthropic, Azure ML, OpenAI, Vertex AI).

- Strong understanding of data structures, algorithms, and statistical modeling.

- Experience building and maintaining data pipelines using tools like Apache Spark, Kafka, Airflow, or cloud-native alternatives.

- Knowledge of containerization and orchestration (Docker, Kubernetes) to deploy 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.

- Excellent problem-solving skills and ability to work independently as well as in cross-functional teams.

- Strong communication skills for collaborating with technical and non-technical stakeholders.

- Knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation.

- Experience with cybersecurity principles related to cloud and AI/ML systems.