MLOps / AI Engineer

Job description

Role Overview

The MLOps / AI Engineer is responsible for designing, building, deploying, and operating production-grade machine learning and AI systems at enterprise scale. This role bridges data science, software engineering, and platform engineering to ensure AI models and applications are reliable, secure, observable, and governed throughout their lifecycle. Key Responsibilities – MLOps & AI Platform Engineering Design, build, and maintain end-to-end MLOps pipelines covering model development, training, validation, deployment, monitoring, retraining, and retirement.

Develop and operate scalable AI/ML platforms across development, test, and production environments. Operationalise machine learning and LLM-based solutions with strong reliability, performance, and governance controls. Support batch and real-time inference workloads. Software Engineering & Automation Develop production-quality services and automation using Python and modern software engineering practices. Build and manage CI/CD pipelines using Git-based workflows.

Implement Infrastructure as Code (IaC) using Terraform, ARM, CloudFormation, or Bicep. Containerisation & Cloud Deployment Deploy AI workloads using Docker and Kubernetes. Design cloud-native architectures on Azure, AWS, or GCP. Implement blue/green, canary, and progressive deployment strategies. LLMs & Generative AI Implement LLM application patterns including prompt orchestration, RAG, embeddings, and vector databases.

Integrate with LLM platforms such as Azure OpenAI, OpenAI, Anthropic, or AWS Bedrock. Work with agentic AI frameworks and orchestration tools. Monitoring, Observability & Operations Implement logging, metrics, tracing, and alerting for AI services. Monitor model performance, drift, latency, and availability. Provide on-call production support and incident resolution. Security, Governance & Compliance Implement IAM, secrets management, encryption, and network security.

Ensure compliance with enterprise governance and audit requirements.

Qualifications & Experience

• 5+ years experience in MLOps, AI Engineering, or Platform Engineering.
• Strong Python and Linux experience.
• Experience deploying AI systems in production environments.

Preferred Skills

• Experience with ML platforms such as MLflow, Kubeflow, SageMaker, or Azure ML.(These are MLOps Frameworks)
• Experience with high-availability AI platforms.
• Familiarity with ITIL or enterprise service management processes.