ML/AI Engineer

Location: Remote (USA)
Employment Type: Contract (W2)
Duration: Long-Term

About the Role

We are seeking a highly skilled Machine Learning / AI Engineer with hands-on experience building and deploying production-grade AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), PyTorch, Transformer architectures, and MLOps, with a proven track record of delivering scalable machine learning systems.

This role involves designing, training, fine-tuning, evaluating, and deploying state-of-the-art machine learning models while optimizing inference performance for real-world production environments.

Key Responsibilities

• Design, train, fine-tune, and deploy Machine Learning and Large Language Models (LLMs).

• Fine-tune foundation models using LoRA, PEFT, and Full Fine-Tuning techniques.

• Build scalable inference pipelines optimized for latency, throughput, and cost.

• Implement model optimization techniques such as quantization, batching, caching, and model serving optimization.

• Design high-quality datasets and data pipelines for model training and evaluation.

• Perform rigorous model benchmarking, offline/online evaluation, regression testing, and performance monitoring.

• Collaborate with Platform Engineers and DevOps teams to deploy AI models into production.

• Monitor model performance and continuously improve accuracy, efficiency, and reliability.

• Stay current with the latest AI research and apply new techniques to production systems.

Required Qualifications

• Bachelor''''''''s or Master''''''''s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

• 3+ years of hands-on experience in Machine Learning or AI Engineering.

• Strong programming experience with Python.

• Hands-on expertise with PyTorch and Transformer architectures.

• Experience fine-tuning LLMs using LoRA, PEFT, or Full Fine-Tuning.

• Experience deploying machine learning models into production environments.

• Strong understanding of model evaluation, benchmarking, and inference optimization.

• Experience with Hugging Face Transformers and modern LLM ecosystems.

• Knowledge of experiment tracking, model versioning, and MLOps best practices.

Preferred Qualifications

• Experience with distributed training using DeepSpeed, FSDP, or multi-GPU environments.

• Experience with Retrieval-Augmented Generation (RAG), embeddings, and vector databases.

• Knowledge of Prompt Engineering and Generative AI applications.

• Experience with Docker, Kubernetes, or cloud platforms (AWS, Azure, or Google Cloud Platform).

• Open-source contributions, research publications, or Kaggle competition experience are a plus.

Technical Skills

• Python

• PyTorch

• Hugging Face Transformers

• Large Language Models (LLMs)

• Transformer Architecture

• LoRA / PEFT

• Fine-Tuning

• Prompt Engineering

• MLOps

• Model Deployment

• Inference Optimization

• Quantization

• Model Evaluation

• DeepSpeed

• FSDP

• Multi-GPU Training

• RAG

• Embeddings

• Git

• Linux

Ideal Candidate

We''''''''re looking for someone who has gone beyond building notebooks or proof-of-concepts and has successfully delivered production AI systems. The ideal candidate should be comfortable owning the complete ML lifecycle—from data preparation and model training to deployment, monitoring, optimization, and continuous improvement.

If you''''''''re passionate about building next-generation AI applications and have hands-on experience with modern LLM technologies, we''''''''d love to hear from you.