Job Title: Senior DevOps Engineer – Generative AI Applications
Experience: 10+ Years
Employment Type: Contract / Full-Time
Location: Remote / Hybrid / Onsite
Work Authorization: As applicable
Job Summary
We are seeking a highly experienced Senior DevOps Engineer with 10+ years of experience in DevOps, cloud infrastructure, automation, CI/CD, and container orchestration, along with hands-on experience supporting and deploying Generative AI applications.
The ideal candidate will have strong expertise in cloud platforms, Kubernetes, Docker, Terraform, Git, GitHub, and CI/CD automation, with the ability to build scalable, secure, and reliable infrastructure for AI/ML workloads and LLM-powered applications. Experience implementing MLOps or LLMOps practices, automating AI application deployments, and managing production environments is highly desirable.
Key Responsibilities
Design, implement, and maintain scalable DevOps solutions for Generative AI and AI/ML applications.
Build and manage CI/CD pipelines for application code, AI models, configuration, and infrastructure deployments.
Automate infrastructure provisioning and configuration management using Terraform and Infrastructure as Code (IaC).
Deploy, manage, and optimize containerized applications using Docker and Kubernetes.
Manage cloud infrastructure and AI application deployments across AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform).
Implement deployment automation for LLM-based applications, RAG pipelines, AI APIs, and model-serving services.
Collaborate with AI engineers, ML engineers, data scientists, and application development teams to operationalize AI solutions.
Implement MLOps/LLMOps practices for model deployment, versioning, testing, monitoring, and lifecycle management.
Manage Git and GitHub repositories, branching strategies, pull requests, code reviews, and GitHub Actions workflows.
Configure Kubernetes resources, Helm charts, autoscaling, ingress, secrets, and resource management for AI workloads.
Implement monitoring, logging, alerting, and observability using tools such as Prometheus, Grafana, Datadog, or cloud-native monitoring services.
Optimize cloud resource utilization, GPU infrastructure where applicable, application performance, and operational costs.
Implement security best practices, including IAM, secrets management, network security, vulnerability scanning, and container image security.
Troubleshoot production issues, perform root cause analysis, and improve system availability, reliability, and performance.
Establish disaster recovery, backup, high availability, and operational support procedures.
Develop automation scripts using Python, Bash, or PowerShell to streamline DevOps operations.
Maintain technical documentation, deployment standards, runbooks, and operational procedures.
Required Technical Skills
DevOps and CI/CD
10+ years of overall experience in DevOps, Cloud Engineering, Infrastructure Automation, or related roles.
Strong hands-on experience designing and implementing CI/CD pipelines.
Experience with Git, GitHub, GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
Proficiency in scripting and automation using Python, Bash, or PowerShell.
Cloud Platforms
Strong experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
Experience managing cloud compute, networking, storage, identity, security, and monitoring services.
Knowledge of cloud architecture, high availability, scalability, and cost optimization.
Containers and Kubernetes
Hands-on experience with Docker, container image management, and container registries.
Strong Kubernetes experience, including deployments, services, ingress, autoscaling, networking, and troubleshooting.
Experience with Helm charts and Kubernetes configuration management.
Understanding of container security and production-grade cluster operations.
Infrastructure as Code
Strong experience with Terraform for provisioning and managing cloud infrastructure.
Knowledge of Terraform modules, state management, reusable configurations, and environment management.
Experience with Ansible or similar configuration management tools is desirable.
Generative AI and AI Application Operations
Experience deploying, integrating, or operationally supporting Generative AI applications in cloud environments.
Understanding of Large Language Models (LLMs), model-serving APIs, and AI application architectures.
Familiarity with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and AI application dependencies.
Experience implementing CI/CD workflows for AI applications and, preferably, model artifacts.
Knowledge of MLOps/LLMOps practices, including model versioning, deployment automation, evaluation workflows, and monitoring.
Familiarity with AI services such as Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
Understanding of AI workload scalability, latency, availability, security, and cost management.
Monitoring, Security, and Reliability
Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, Splunk, or cloud-native solutions.
Knowledge of secrets management, IAM, vulnerability scanning, and secure deployment practices.
Experience with incident management, root cause analysis, and production troubleshooting.
Preferred Qualifications
Experience with GPU-enabled infrastructure or Kubernetes-based AI/ML workloads.
Familiarity with MLflow, Kubeflow, KServe, or similar MLOps/model-serving technologies.
Experience with Argo CD, FluxCD, or other GitOps deployment tools.
Knowledge of service mesh technologies such as Istio.
Experience with AI observability, LLM performance monitoring, and inference cost optimization.
Exposure to enterprise AI security, data privacy, and responsible AI deployment practices.
Education
Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.
Ideal Candidate Profile
The ideal candidate will bring 10+ years of DevOps and cloud engineering experience, strong hands-on skills in Kubernetes, Docker, Terraform, GitHub, and CI/CD, and practical knowledge of deploying and operating Generative AI applications. The candidate should be comfortable working with AI/ML teams to build secure, scalable, automated, and production-ready AI platforms
