Senior DevOps Engineer - AI & Agentic Systems

We are seeking a highly skilled Senior DevOps Engineer with experience in architecting and deploying Agentic AI systems. The ideal candidate will have a strong background in DevOps, cloud infrastructure, and modern AI integrations, including LLM-based systems and autonomous agent development.

Requirements

Key Responsibilities:

• Architect AI-Driven Systems: Design and implement modular architectures that integrate large language models (LLMs) and support multi-step reasoning, retrieval-augmented generation (RAG), and external tool usage.
• AI Model Integration: Evaluate, select, and integrate suitable AI models (e.g., GPT-5, custom LLMs) into scalable production environments.
• Agent Development: Build and deploy intelligent agents capable of research, summarization, task automation, and action execution.
• Infrastructure & Scalability: Manage containerized environments using Docker and Kubernetes, ensuring high availability, performance, and security.
• Responsible AI Practices: Establish and maintain content filtering, audit logging, and data protection mechanisms to ensure ethical AI operations.
• Stakeholder Collaboration: Work closely with cross-functional teams to translate business needs into scalable technical AI solutions.
• Continuous Optimization: Monitor agent performance, analyze errors, refine prompts, and optimize system behavior for accuracy and efficiency.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in DevOps or cloud infrastructure engineering.
• Strong hands-on experience with Docker, Kubernetes, and CI/CD pipelines.
• Experience with LLMs, AI model deployment, or MLOps workflows is a strong plus.
• Knowledge of cloud platforms (AWS, Azure, or GCP) and infrastructure as code (Terraform, Ansible, etc.).
• Excellent problem-solving skills and the ability to work in a fast-paced, AI-focused environment.

Preferred Skills:

• Experience with Python-based AI frameworks and APIs.
• Familiarity with retrieval systems, vector databases, or RAG pipelines.
• Understanding of data governance, compliance, and responsible AI principles.