AI Infrastructure Engineer (AWS/LLM/GPU Systems)

Title: AI Infrastructure & Optimization (AWS/LLM/GPU)

Location: Onsite- Bangalore, Hyderabad, Chennai, Pune, Mumbai, Noida, Gurugram

Mandatory skills: 5+ yrs in ML infrastructure, AWS AI/ML platforms, and GPU-based LLM deployment or inference optimization.

Core Qualifications

• Bachelors degree in Computer Science, Engineering, or equivalent practical experience (Master’s/PhD preferred).
• 5+ years of experience in ML infrastructure, model deployment, or GPU computing.
• Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX.
• Strong understanding of LLM architectures, training methodologies, and inference optimization.

Technical Expertise

• Hands-on experience training, fine-tuning, or deploying LLMs in production.
• Experience with GPU programming, CUDA, and kernel-level optimization techniques.
• Experience with distributed training frameworks and multi-GPU/multi-node orchestration.
• Strong knowledge of AWS services including EC2 GPU instances, S3, EFS, VPC, and networking.

Preferred Experience

• Experience with Amazon SageMaker AI (Training, Inference, HyperPod) or equivalent ML platforms.
• Understanding of NVIDIA GPU architectures (A100, H100) and AWS Trainium/Inferentia.
• Experience with model optimization techniques such as quantization, pruning, and distillation.
• Knowledge of MLOps, model monitoring, and production ML system design.
• Background in distributed systems, high-performance computing, or systems programming.

Essential Attributes

• Strong debugging and problem-solving skills for complex infrastructure issues.
• Analytical mindset with a data-driven approach to optimization.
• Ability to explain complex technical concepts to diverse stakeholders.
• Comfortable working in fast-paced, evolving environments.
• Strong ownership mindset with ability to drive projects from architecture to production.

Interested candidates can apply here or share the resume to hr@lancetechsolutions.com