Job Title: AI DevOps EngineerJob SummaryWe are seeking an AI DevOps Engineer to build, automate, and manage the infrastructure and deployment pipelines that support AI and machine learning applications. This role bridges DevOps, MLOps, and cloud engineering to ensure reliable, secure, and scalable delivery of AI solutions. The ideal candidate has expertise in cloud platforms, container orchestration, CI/CD, Infrastructure as Code (IaC), and observability, with experience supporting production AI and Generative AI workloads.
Key Responsibilities- Design, implement, and maintain CI/CD pipelines for AI and machine learning applications.
- Automate the deployment, scaling, and lifecycle management of AI services across cloud and hybrid environments.
- Build and manage containerized AI workloads using Docker and Kubernetes.
- Provision and manage infrastructure using Infrastructure as Code (Terraform, Pulumi, or CloudFormation).
- Deploy and support AI/ML models in production using model serving frameworks and MLOps tools.
- Monitor application health, infrastructure performance, model availability, and operational metrics.
- Implement logging, tracing, and observability solutions for AI platforms.
- Optimize cloud resources, GPU utilization, and infrastructure costs.
- Integrate security best practices, secrets management, identity management, and compliance controls into AI deployment pipelines.
- Collaborate with AI engineers, ML engineers, data engineers, platform engineers, and software development teams to streamline AI delivery.
- Support automated testing, release management, rollback strategies, and disaster recovery for AI systems.
- Troubleshoot production issues related to AI infrastructure, deployments, networking, and performance.
- Continuously improve deployment automation, reliability, scalability, and operational efficiency.
Required Qualifications- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
- 4+ years of experience in DevOps, Cloud Engineering, Platform Engineering, or Site Reliability Engineering (SRE).
- Experience supporting AI, machine learning, or data platforms in production environments.
- Strong programming and scripting skills in Python and Bash; Go is a plus.
- Hands-on experience with Docker and Kubernetes.
- Experience with AWS, Microsoft Azure, or Google Cloud Platform.
- Experience building CI/CD pipelines using GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
- Experience with Infrastructure as Code tools such as Terraform or Pulumi.
- Strong understanding of Linux administration, networking, and cloud security.
- Experience with monitoring and observability tools such as Prometheus, Grafana, OpenTelemetry, ELK Stack, or Splunk.
Preferred Qualifications- Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure Machine Learning.
- Experience deploying Large Language Models (LLMs) and Generative AI applications.
- Knowledge of model serving frameworks such as KServe, NVIDIA Triton Inference Server, Ray Serve, or BentoML.
- Experience with vector databases and Retrieval-Augmented Generation (RAG) architectures.
- Familiarity with Apache Airflow, Kafka, Ray, or Apache Spark.
- Experience with GPU-enabled infrastructure and NVIDIA CUDA environments.
- Understanding of AI governance, Responsible AI, and model monitoring practices.
Technical Skills- Python
- Bash
- Go (preferred)
- Docker
- Kubernetes
- Terraform / Pulumi / CloudFormation
- Git
- GitHub Actions
- GitLab CI
- Azure DevOps
- Jenkins
- AWS / Azure / Google Cloud Platform
- Linux
- Prometheus
- Grafana
- OpenTelemetry
- ELK Stack
- Splunk
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Azure Machine Learning
- KServe
- NVIDIA Triton Inference Server
- Ray Serve
- BentoML
- PostgreSQL
- Redis
Soft Skills- Strong analytical and troubleshooting skills
- Excellent communication and cross-functional collaboration
- Ownership and accountability
- Ability to thrive in Agile and DevOps environments
- Strong automation mindset
- Continuous learning and adaptability
Nice to Have- Experience with LLMOps and Generative AI deployment pipelines
- Experience implementing GitOps using Argo CD or Flux
- Knowledge of service mesh technologies such as Istio or Linkerd
- Experience with FinOps and cloud cost optimization
- Cloud, Kubernetes, or DevOps certifications (AWS, Azure, GCP, CKA, CKAD, Terraform)
LocationHybrid / Remote / On-site (as applicable)
Employment TypeFull-time
