Posted 29 May, 2026
Ai infrastructure automation engineer
TECEZE
Bengaluru, KA, IN
Full Time
Reference: dee162189724a1da
Job Description
Role Overview
We are seeking a highly skilled Infrastructure Engineer with expertise in AI-driven automation to design, build, and optimize intelligent solutions. The ideal candidate will have hands-on experience integrating large language models and AI platforms into infrastructure workflows, enabling enhanced automation, decision support, and operational efficiency.
Key Responsibilities
Design and develop AI-powered automation and optimization solutions using platforms such as Microsoft Copilot and Google Gemini.
Build and integrate intelligent agents, scripts, microservices, and automation frameworks that interface with AI/LLM models for data enrichment and decision-making.
Develop custom prompts, workflows, and extensions to tailor AI behavior for infrastructure and operational use cases.
Implement retrieval-augmented generation (RAG) and prompt engineering techniques to enhance AI solution accuracy and relevance.
Translate complex infrastructure challenges into scalable AI-driven solutions.
Collaborate with cross-functional teams to drive innovation in infrastructure automation and AI adoption.
Continuously evaluate emerging AI technologies and apply them to improve system performance and efficiency.
Required Skills and Experience
Strong experience in infrastructure engineering (cloud, systems, or platform engineering).
Hands-on experience with AI/LLM integrations, including Microsoft Copilot and Google Gemini.
Proficiency in scripting and automation using tools such as Python, Bash, Power Shell, Terraform, and/or Ansible.
Solid understanding of prompt engineering and RAG frameworks.
Experience working with Google Cloud Platform (GCP).
Strong analytical and problem-solving skills with the ability to design practical, scalable solutions.
Ability to work effectively in fast-paced, innovation-driven environments.
Preferred Qualifications
Experience building production-grade AI-powered automation platforms.
Familiarity with APIs, microservices architecture, and workflow orchestration.
Exposure to Dev Ops, SRE, or platform engineering practices.
We are seeking a highly skilled Infrastructure Engineer with expertise in AI-driven automation to design, build, and optimize intelligent solutions. The ideal candidate will have hands-on experience integrating large language models and AI platforms into infrastructure workflows, enabling enhanced automation, decision support, and operational efficiency.
Key Responsibilities
Design and develop AI-powered automation and optimization solutions using platforms such as Microsoft Copilot and Google Gemini.
Build and integrate intelligent agents, scripts, microservices, and automation frameworks that interface with AI/LLM models for data enrichment and decision-making.
Develop custom prompts, workflows, and extensions to tailor AI behavior for infrastructure and operational use cases.
Implement retrieval-augmented generation (RAG) and prompt engineering techniques to enhance AI solution accuracy and relevance.
Translate complex infrastructure challenges into scalable AI-driven solutions.
Collaborate with cross-functional teams to drive innovation in infrastructure automation and AI adoption.
Continuously evaluate emerging AI technologies and apply them to improve system performance and efficiency.
Required Skills and Experience
Strong experience in infrastructure engineering (cloud, systems, or platform engineering).
Hands-on experience with AI/LLM integrations, including Microsoft Copilot and Google Gemini.
Proficiency in scripting and automation using tools such as Python, Bash, Power Shell, Terraform, and/or Ansible.
Solid understanding of prompt engineering and RAG frameworks.
Experience working with Google Cloud Platform (GCP).
Strong analytical and problem-solving skills with the ability to design practical, scalable solutions.
Ability to work effectively in fast-paced, innovation-driven environments.
Preferred Qualifications
Experience building production-grade AI-powered automation platforms.
Familiarity with APIs, microservices architecture, and workflow orchestration.
Exposure to Dev Ops, SRE, or platform engineering practices.
