DevOps Engineer (AI/ML)

TL;DR

DevOps Engineer (AI/ML): Design and implement CI/CD pipelines for AI/ML model training, RAG systems, and agentic workflows with an accent on infrastructure provisioning, container orchestration, and observability. Focus on optimizing GPU workloads, securing deployments, and enabling smooth production transitions for scalable enterprise AI platforms.

Location: Alpharetta, Georgia, USA / Columbus, Georgia, USA. Candidates must be legally authorized to work in the United States on a full-time basis without sponsorship. Not accepting H1B or OPT status.

Company

hirify.global Inc., a payments technology company supporting AI and ML initiatives enterprise-wide.

What you will do

• Design and implement CI/CD pipelines for AI/ML model training, evaluation, RAG deployments, LLMs, vectorDBs, and governance systems.
• Provision and manage AI infrastructure on AWS/GCP using Terraform and infrastructure-as-code.
• Maintain Docker/Kubernetes environments optimized for GPU workloads and distributed compute.
• Deploy and support vector databases, feature stores like pgVector, Pinecone, Redis, Featureform, MongoDB Atlas.
• Monitor and optimize AI workload performance, availability, cost with Prometheus, Grafana, Datadog.
• Collaborate with data scientists and AI/ML engineers for experimentation to production transitions.
• Implement security practices: secrets management, access control, encryption, audit logging.
• Support agentic AI systems with LangChain, LangGraph, CrewAI, Copilot Studio.

Requirements

• 6+ years DevOps/infrastructure experience, preferably 2+ years in AI/ML.
• Hands-on with cloud-native AI services (AWS Bedrock/SageMaker, GCP Vertex AI) and GPU management.
• Strong CI/CD (GitHub Actions, ArgoCD, Jenkins) and config management (Ansible, Helm).
• Proficient in Python, Bash; Go nice to have.
• Experience with monitoring/logging/alerting for AI/ML workloads.
• Deep Kubernetes and container lifecycle knowledge.

Nice to have

• MLOps tools: MLflow, Kubeflow, SageMaker/Vertex Pipelines.
• Prompt engineering, model fine-tuning, inference serving.
• Secure AI deployment and compliance frameworks.
• Model versioning, drift detection, scalable rollback.

Culture & Benefits

• Full-time position with expected standard hours.
• Collaborative environment with data scientists, AI/ML engineers, platform team.
• Focus on initiative, accuracy, prioritization, deadlines, professional interactions.
• High flexibility, critical thinking, independent work with minimal supervision.