AI Infrastructure Engineer

Job Description:

Immediate joiners or less than 30 days notice period.

Job Summary:

We are looking for an AI Infrastructure Engineer with 12+ years of total experience and 2 - 4 years of hands-on experience in AI/ML infrastructure or MLOps. The role focuses on designing, scaling, and optimizing AI platforms, cloud infrastructure, automation, and reliable ML/LLM deployment environments.

Key Responsibilities:

• Design, build, and maintain scalable infrastructure, pipelines, and environments for AI/ML model training and deployment.

• Partner with Infrastructure Operations teams to identify operational bottlenecks and implement AI-driven automation.

• Optimize GPU/CPU workloads, storage, and compute clusters for performance and cost efficiency.

• Translate business and operational requirements into AI infrastructure initiatives.

• Measure infrastructure improvements through uptime, latency, cloud cost, and productivity metrics.

• Establish MLOps and LLMOps practices including CI/CD, monitoring, drift detection, and model governance.

• Implement observability and telemetry for AI models and underlying infrastructure.

• Develop reliable, scalable, and resilient AI infrastructure solutions.

Mandatory Skills:

• 12+ years of experience in Software or Infrastructure Engineering.

• 2 - 4 years of experience in AI/ML infrastructure or MLOps.

• Strong experience in AWS or Azure cloud architecture.

• Hands-on experience with cloud networking, IAM, security, and infrastructure architecture.

• Strong experience with Terraform/IaC and infrastructure automation.

• Hands-on experience with Kubernetes and Docker.

• Proficiency in Python or Go automation and strong Linux/shell scripting skills.

• Experience with observability and ITSM integration.

• Experience integrating AI/LLM APIs into IT operations.

• Exposure to RAG and AI-driven automation solutions.

AI/ML & MLOps Skills:

• Experience deploying AI/ML frameworks such as PyTorch and TensorFlow.

• Experience with vector databases.

• Hands-on experience with MLOps tools such as Kubeflow, MLflow, Ray, or Triton Inference Server.

• Understanding of LLM infrastructure, model deployment, and AI platform scalability.

Preferred Skills:

• Experience with LLM infrastructure and fine-tuning environments.

• Experience building and scaling RAG pipelines.