Machine Learning Operations Specialist | AI Platform Engineer

Overview

We are seeking a skilled Machine Learning Operations Specialist and AI Platform Engineer to design, develop, and maintain scalable infrastructure environments for AI and machine learning workloads. This full-time role involves collaboration with Data Scientists, ML Engineers, Software Engineers, DevOps, Cloud, and Security teams to support the complete machine learning lifecycle from creation to deployment and monitoring.

Key Responsibilities

• Architect and maintain scalable infrastructure to facilitate machine learning and AI workflows.
• Create and manage platforms that enable model development, training, deployment, monitoring, and lifecycle management.
• Develop automated MLOps pipelines for tasks such as model training, validation, deployment, and retraining.
• Implement CI/CD and continuous integration strategies specific to machine learning models and related applications.
• Oversee model versioning, artifact management, experiment tracking, and ensure result reproducibility.
• Automate machine learning workflows through suitable orchestration and pipeline technologies.
• Deploy models to production environments whether cloud-based, on-premise, or hybrid setups.
• Design and operate scalable serving infrastructures capable of handling high-volume inference requests.
• Continuously monitor model and system performance including health, resource utilization, latency, availability, and reliability.
• Implement monitoring solutions to detect model drift, data drift, evaluate prediction quality and overall production effectiveness.
• Create automated alerts and remediation workflows for addressing model and platform anomalies.
• Work closely with Data Scientists to move experimental models into production-ready solutions.
• Collaborate with ML Engineers to enhance deployment robustness, scalability, and operational efficiency.
• Support GPU and high-performance computing resources utilized in AI and ML projects.
• Optimize cloud, compute, storage, and networking infrastructure used by AI platforms.
• Leverage Infrastructure as Code practices to automate AI infrastructure provisioning and management.
• Manage containerized machine learning environments using Docker, Kubernetes, and Helm.
• Build reusable platform components and develop self-service tools for data science and ML teams.
• Standardize environments for development, staging, testing, and production models.
• Implement infrastructure security measures including access controls, secrets management, and encryption.
• Ensure data and model governance compliance throughout the ML lifecycle.
• Maintain deployment standards, operational runbooks, and comprehensive technical documentation.
• Diagnose and resolve issues related to model serving, infrastructure, pipelines, deployment, and overall system performance.
• Conduct root cause analysis and implement sustainable solutions for recurring problems in ML platforms.
• Support cloud migrations, AI platform modernization efforts, and large-scale machine learning projects.
• Assess new MLOps tools, AI infrastructure technologies, model serving platforms, and automation frameworks.
• Establish and track platform reliability and operational metrics for AI and machine learning services.
• Implement disaster recovery, backup, high availability, and business continuity plans for critical AI platforms.
• Collaborate with cybersecurity to guarantee AI infrastructure adheres to organizational security policies.
• Continuously improve automation, scalability, monitoring, reliability, and developer experience within the AI platform.

Essential Qualifications and Experience

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software or Data Engineering, or related disciplines.
• Prior experience in MLOps, Machine Learning Engineering, AI Platform Engineering, DevOps, or Cloud Engineering roles.
• Deep understanding of the machine learning lifecycle and production ML systems management.
• Practical expertise in deploying and maintaining machine learning models in production environments.
• Familiarity with MLOps platforms and tools such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Azure ML or similar.
• Strong experience working with cloud providers like AWS, Microsoft Azure, or Google Cloud.
• Proficiency with container technologies including Docker, Kubernetes, and Helm.
• Solid knowledge of CI/CD pipelines, Git workflows, automation, and modern DevOps best practices.
• Hands-on experience applying Infrastructure as Code using Terraform, CloudFormation, Pulumi, or equivalent.
• Programming or scripting skills in Python, Bash, Go or related languages.
• Understanding of machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Experience with model serving, APIs, inference infrastructure, and production system operations.
• Knowledge of model and data drift, performance monitoring, observability, and monitoring toolsets.
• Awareness of distributed systems, cloud infrastructure architecture, networking, storage, and computing resources.
• Experience with GPU and accelerated computing infrastructure for AI is a plus.
• Familiarity with data pipelines, feature stores, model registries, and experiment tracking systems.
• Understanding of AI security, secrets management, access control, and data protection.
• Strong problem-solving, debugging, and root-cause analysis abilities.
• Experience using monitoring and observability platforms like Prometheus, Grafana, or Datadog.
• Knowledge of designing highly available, scalable, and fault-tolerant infrastructure.
• Excellent technical writing and effective communication skills.
• Ability to collaborate efficiently with cross-functional teams including Data Science, ML Engineering, DevOps, Security, and business stakeholders.
• Capability to handle multiple technical projects with competing priorities in dynamic environments.
• Relevant certifications in cloud platforms, Kubernetes, AI, ML, or MLOps will be considered advantageous.

Compensation and Benefits

• Competitive remuneration with comprehensive benefits package.
• Chance to build and maintain modern AI and machine learning platforms.
• Exposure to technologies such as MLOps, cloud computing, Kubernetes, automation, and AI infrastructure.
• Opportunities for professional growth in machine learning operations, AI engineering, cloud architecture, and DevOps disciplines.
• Access to modern AI platforms, GPU hardware, and a wide range of MLOps tools.
• Possibility to lead significant AI platform initiatives.
• Involvement in cutting-edge machine learning and artificial intelligence projects.
• Support for attaining industry certifications in cloud, AI, Kubernetes, and related areas.
• Collaborative environment fostering innovation, automation, reliability, and scalability.
• Recognition and career path advancement towards roles like Senior MLOps Specialist, AI Platform Engineer, MLOps Manager, AI Infrastructure Lead, or Head of AI Platform Engineering.