Lead AI Infrastructure Engineer

This role is for one of our clients

Industry : Technology, Information and Media

Seniority level : Mid-Senior level

Min Experience : 6 years

Location : Remote (India)

JobType : full-time

We are seeking a highly skilled Lead AI Infrastructure Engineer to drive the development and management of our AI and ML infrastructure. This role blends technical leadership with hands-on execution, overseeing the end-to-end ML lifecycle — from model training and deployment to monitoring, optimization, and scaling. You will lead a small team of engineers while ensuring seamless collaboration between research, engineering, and operations teams.

Key Responsibilities

ML Infrastructure & Lifecycle Management

Design, maintain, and optimize scalable infrastructure for ML training, inference, and experimentation.

Ensure model deployment pipelines are reliable, efficient, and cost-effective.

Implement robust monitoring, alerting, and automated rollback mechanisms to maintain system reliability.

Collaboration with Research & Product Teams

Partner with research teams to streamline workflows for training, evaluation, and fine-tuning of models.

Support AI-driven initiatives across product teams by providing reliable infrastructure and operational expertise.

Team Leadership & Mentorship

Lead a small team of ML engineers, providing guidance, mentoring, and technical support.

Balance hands-on engineering work with strategic oversight of infrastructure projects.

Performance & Optimization

Enhance model inference latency, throughput, and cost-efficiency.

Apply model optimization techniques such as quantization, distillation, and TensorRT integration.

Automation & Best Practices

Develop and enforce CI / CD practices for ML models, including versioning, testing, and deployment.

Establish MLOps standards and operational excellence across teams.

Cloud & Platform Management

Leverage cloud-based ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) to optimize workflows and costs.

Maintain secure, compliant, and scalable AI environments for both training and inference workloads.

Architecture & Strategy

Contribute to ML architecture design, documentation, and roadmap planning.

Continuously evaluate emerging AI infrastructure technologies to improve efficiency and performance.

Qualifications & Skills

5+ years of hands-on experience in MLOps, ML Engineering, or AI Infrastructure roles.

Strong understanding of ML / DL concepts with applied experience in model training and deployment.

Proficiency with cloud-native ML platforms : AWS SageMaker, GCP Vertex AI, or Azure ML.

Experience with Kubernetes, Docker, MLflow, Kubeflow, or similar orchestration tools.

Familiarity with model optimization techniques : quantization, distillation, TensorRT, FasterTransformer.

Proven ability to lead technical projects and mentor engineers in a fast-paced environment.

Excellent communication and cross-functional collaboration skills.

Ownership-driven mindset and ability to bring clarity to ambiguous technical challenges.

Core Skills

MLOps | ML Infrastructure | Model Deployment | Model Monitoring | CI / CD for ML | Cloud ML Platforms | Kubernetes | Docker | Vertex AI | AWS SageMaker | Kubeflow | MLflow | Model Optimization

Skills Required

MLops, Model Monitoring, Docker, Kubernetes