We are looking for a Lead MLOps Engineer to support the development and deployment of AI tools.

This role bridges data science, engineering, and cloud infrastructure, focusing on building scalable and automated production-grade AI systems. You will work closely with data scientists, business stakeholders, and cloud engineers to convert prototypes into dependable, high-performance applications that drive predictive analytics, generative AI solutions, and interactive dashboards for business leaders. If you have substantial experience in MLOps and cloud infrastructure and enjoy collaborating across teams, we encourage you to apply.

Responsibilities

Collaborate with data scientists to convert ML and generative AI experiments into scalable production pipelines
Develop and maintain shared code repositories and reusable components
Design and implement CI/CD pipelines in AWS or Azure environments for deploying models, APIs, and generative AI tools
Build and manage data pipelines and DataOps processes
Containerize applications using Docker and deploy them with cloud-native services
Automate infrastructure provisioning using Terraform and manage database schemas in Azure and Snowflake
Deploy and administer generative AI applications such as chatbots, retrieval-augmented generation (RAG) systems, and predictive tools
Implement monitoring, observability, and explainability solutions to ensure system stability and performance
Establish alerting, rollback strategies, and observability frameworks to maintain operational stability
Participate in code reviews and recommend improvements to workflows and processes

Requirements

Experience of 8 to 12 years in MLOps or DevOps engineering
Proven leadership experience in managing AI or ML deployment projects
Background in collaborating with data scientists and cloud engineers
Expertise in building CI/CD pipelines in AWS or Azure platforms
Skills in containerization using Docker and deploying applications in cloud environments
Proficiency in infrastructure automation using Terraform
Knowledge of managing data schemas in cloud data warehouses such as Azure and Snowflake
Understanding of generative AI fundamentals and applications
Capability to deploy and manage AI models and APIs in production

Nice to have

Familiarity with large language models (LLM)
Experience with retrieval-augmented generation (RAG) systems