AI/ML Engineer

AI/ML Engineer

• Summary Build, train, and deploy machine learning models — with a focus on LLM fine-tuning and high-performance inference pipelines. You own models from experimentation through to reliable production serving.

Role Expectations

• Train, fine-tune, and evaluate models — including LLMs via full fine-tuning, LoRA/PEFT, and instruction tuning.
• Build and optimize inference pipelines for latency, throughput, and cost (batching, quantization, caching).
• Design data pipelines and datasets for training and evaluation; ensure data quality and reproducibility.
• Implement rigorous offline and online evaluation, benchmarking, and regression testing for models.
• Partner with platform engineers to deploy models to production with monitoring and rollback.
• Stay current with model architectures and translate research into practical improvements.

Required Skills

• 5+ years in ML/AI engineering; proven track record shipping models to production.
• Strong Python and deep learning with PyTorch; solid understanding of the Transformer architecture.
• Hands-on LLM fine-tuning experience (LoRA/PEFT, quantization, or full fine-tuning).
• Experience building inference/serving pipelines and measuring model performance.
• Comfortable with experiment tracking, versioning, and MLOps fundamentals.
• Value adds:
• Distributed training experience (multi-GPU, DeepSpeed, FSDP).
• Publications, competition results, or notable open-source ML work.
• Experience with retrieval, embeddings, and evaluation frameworks.

Core Stack:

• Python
• PyTorch
• Transformers
• LLM Fine-Tuning
• MLOps 
• Prompt Engineering