Job Description

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.