AI Devops Engineer
Job Description
Supercharge Your Career as a AI DevOps Engineer at Technoidentity!
At Technoidentity, we're a Data & AI product engineering company with over 15 years of expertise in building durable digital products, intelligent enterprise solutions, and scalable Data & AI platforms. As we continue expanding globally, it's the perfect time to join our team of tech innovators and make a lasting impact.
What’s in it for You?
We are looking for an AI DevOps Engineer with 0–3 years of experience who is passionate about AI, Cloud, DevOps, and Automation. The role involves building, deploying, and managing AI-powered applications, LLM solutions, and cloud-native platforms while ensuring reliability, scalability, security, and observability.
What Will You Be Doing?
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- Develop and deploy AI/ML and Generative AI solutions using Python.
\n - Build applications leveraging LLMs, RAG, and AI agents.
\n - Create and maintain CI/CD pipelines for AI applications.
\n - Deploy and manage workloads using Docker and Kubernetes.
\n - Support cloud platforms (AWS, Azure, or GCP).
\n - Implement Infrastructure as Code (Terraform) and automation workflows.
\n - Monitor applications using observability tools such as Prometheus, Grafana, and logging platforms.
\n - Collaborate with engineering teams to ensure system reliability, performance, and security.
\n - Contribute to MLOps practices, AI accelerators, and reusable frameworks.
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\nRequirements \n
What Makes You the Perfect Fit?
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- Python programming (mandatory)
\n - Understanding of Machine Learning, LLMs, Prompt Engineering, and RAG
\n - Experience with OpenAI, LangChain, LlamaIndex, or Hugging Face
\n - Docker, Kubernetes, Git, and CI/CD tools
\n - AWS, Azure, or GCP
\n - PostgreSQL; MongoDB and Vector Databases are a plus
\n - Basic knowledge of MLOps, Terraform, and workflow orchestration tools (Airflow/Temporal)
\n - Familiarity with observability and monitoring tools
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- Bachelor's degree in Computer Science, AI, Data Science, IT, or related field
\n - 0–3 years of experience in AI/ML, Software Engineering, Cloud, DevOps, or related areas
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- Experience with Agentic AI frameworks
\n - Knowledge of MLOps and AI platform operations
\n - Exposure to enterprise-grade monitoring, reliability engineering, and security best practices
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