AI Engineer (AI+ Azure DevOps)

Job Title : AI Engineer (AI+ Azure DevOps)

Experience :3+ years

Notice Period : Immediate Joiner

About the Role We are seeking an AI Engineer with a strong DevOps and Cloud Engineering background to design, build, deploy, and operate enterprise-grade AI solutions. This role will focus on developing Generative AI applications, integrating LLMs, managing AI infrastructure, automating deployments, and ensuring reliability, scalability, security, and observability of AI platforms. The ideal candidate combines software engineering, AI development, cloud architecture, and DevOps practices to deliver production-ready AI solutions. Key Responsibilities Design, develop, and deploy AI/ML and Generative AI solutions using OpenAI, Azure OpenAI, Anthropic, or similar platforms. Build AI applications leveraging RAG, vector databases, embeddings, agents, and prompt orchestration frameworks. Develop and maintain scalable AI platforms on Azure, AWS, or GCP. Implement CI/CD pipelines for AI applications and machine learning workflows. Build Infrastructure as Code (IaC) using Terraform, Bicep, ARM, or CloudFormation. Manage containerized workloads using Docker and Kubernetes. Automate model deployment, testing, monitoring, and rollback processes. Integrate AI services with enterprise applications, APIs, and business workflows. Implement AI observability, monitoring, logging, and performance tracking. Ensure security, governance, and compliance for AI platforms and data usage. Collaborate with product, engineering, platform, and security teams to deliver AI solutions. Troubleshoot production AI workloads and optimize performance, reliability, and cost. Required Skills 3+ years of experience in Software Engineering, AI Engineering, DevOps, or Platform Engineering. Strong programming skills in Python. Experience with Generative AI technologies and LLM platforms. Hands-on experience with OpenAI, Azure OpenAI, LangChain, LlamaIndex, Semantic Kernel, or similar frameworks. Strong knowledge of REST APIs and microservices architecture. Experience with Azure, AWS, or GCP. Strong understanding of CI/CD tools such as GitHub Actions, Azure DevOps, Jenkins, or GitLab CI/CD. Experience with Terraform, Bicep, ARM Templates, or CloudFormation. Hands-on experience with Docker and Kubernetes. Knowledge of monitoring and observability tools such as Grafana, Prometheus, Datadog, Azure Monitor, or ELK. Experience implementing security best practices in cloud-native environments. Understanding of networking fundamentals, authentication, OAuth, SSO, and API security. Preferred Qualifications Experience building enterprise AI platforms. Knowledge of RAG architectures and vector databases such as Pinecone, Weaviate, Chroma, or Azure AI Search. Experience with MLOps tools including MLflow, Azure ML, SageMaker, or Vertex AI. AI/Cloud certifications. Experience supporting multi-tenant AI environments. Exposure to FinOps or AI cost optimization. What Makes Someone Successful Automation-first mindset. Strong ownership and accountability. Excellent troubleshooting and problem-solving skills. Passion for AI innovation and emerging technologies. Strong communication and stakeholder management skills. Ability to operate across development, operations, security, and business teams. Focus on scalability, reliability, and operational excellence.