Job Description

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 u007C ML Infrastructure u007C Model Deployment u007C Model Monitoring u007C CI/CD for ML u007C Cloud ML Platforms u007C Kubernetes u007C Docker u007C Vertex AI u007C AWS SageMaker u007C Kubeflow u007C MLflow u007C Model Optimization