DevOps (MLOps) Engineer

We are seeking a highly skilled and experienced DevOps Engineer to join our team.

This role is ideal for someone with a strong background in automating and optimizing infrastructure, deploying scalable systems, and ensuring reliability across complex environments. You’ll play a critical role in shaping the tools, practices, and standards that drive our CI/CD pipelines, cloud infrastructure, and overall system performance. As a key member of our team, you’ll collaborate closely with engineering, operations, and product teams to ensure seamless delivery and high uptime of our services.

Responsibilities

Implement and Maintain CI/CD Pipelines

• Establish and maintain CI/CD pipelines to ensure smooth operation for AI and machine learning projects.

Ensure Robust Deployment Strategies

• Develop and implement reliable deployment strategies for AI models, with a focus on LLMs and Retrieval-Augmented Generation (RAG).

Monitor AI Application Performance

• Monitor the performance, reliability, and availability of deployed AI models to ensure optimal operation.

Collaborate with AI Research Teams

• Assist in transitioning machine learning models and algorithms into production environments efficiently.

Develop and Enforce Best Practices

• Develop and enforce best practices for version control, configuration management, and testing of AI-driven solutions.

Utilize MLOps Tools

• Leverage frameworks such as Kubeflow, MLflow, or TensorFlow Extended (TFX) to manage the ML lifecycle from experimentation to production.

Implement Monitoring Solutions

• Implement monitoring solutions for infrastructure metrics and AI model performance to enable proactive issue detection.

Participate in On-Call Rotations

• Participate in on-call rotations to ensure uptime and meet service-level objectives (SLOs).

Requirements

Bachelor’s Degree

• A degree in Computer Science, Engineering, or a related field, or equivalent experience.

Experience in DevOps or SRE

• Proven experience as a DevOps Engineer or Site Reliability Engineer (SRE), with a strong emphasis on software development and automation.

Expertise in LLM Deployment

• Proven expertise in managing and deploying Large Language Models (LLMs), particularly using Retrieval-Augmented Generation (RAG) techniques.

Proficiency in CI/CD Tools

• Hands-on experience with CI/CD tools such as Jenkins, GitLab CI, CircleCI, Travis CI, Bamboo, or TeamCity.

• Experience with Infrastructure as Code (IaC) tools like Terraform, Ansible, or Chef.

Container Orchestration Knowledge

• Strong understanding of Kubernetes, Docker, and other container orchestration technologies.

Familiarity with MLOps

• Competence with MLOps tools to facilitate machine learning development and deployment.

Cloud Service Expertise

• Experience with cloud platforms such as AWS, GCP, or Azure, particularly in AI/ML environments.

Monitoring Tools Experience

• Skilled in monitoring tools like Prometheus, Grafana, and the ELK (Elasticsearch, Logstash, and Kibana) stack.

Python Knowledge

• Strong understanding of Python, particularly in the context of data science and machine learning.

Relevant Certifications

• Certifications in Kubernetes, AWS/GCP/Azure, or related technologies are a plus.

If you're passionate about automation, cloud infrastructure, and building reliable, scalable systems, and eager to work in a dynamic, fast-paced environment, we encourage you to apply!