Machine Learning Engineer (W2 Only)

Required Skills & Experience Able to work w2 without sponsorship • 5+ years implementing AI solutions in cloud environments with focus on AI-services and MLOps • 3+ years hands-on experience with ML model development and production infrastructure • Proven track record delivering production ML systems in enterprise environments • ML & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM • Cloud & Infrastructure: EKS, S3, SageMaker, Docker/Kubernetes, Terraform/Cloud Formtion. AWS services (EC2, EKS, S3, SageMaker), Terraform/CloudFormation, Docker, Kubernetes • Data & Processing: Python, SQL, PySpark, Apache Spark, Airflow, Kinesis, feature stores, model serving frameworks • Development & Operations: Streaming/batch architectures at scale, DevOps, CI/CD (GitHub Actions, CodePipeline), monitoring (CloudWatch, Prometheus, MLflow) • Agile Methodology experience • End-to-end ML systems experience from research to production • Strong communication and collaboration skills • Ability to work independently with minimal supervision • Enterprise security and compliance experience Nice to Have Skills & Experience • Recommendation systems, NLP applications, or real-time inference systems experience • MLOps platform development and feature store implementations • Hospitality • GCP Job Description Our client seeks an experienced Machine Learning Engineer contractor to build algorithmic assets across Personalization, Generative AI, Forecasting, and Decision Science domains. This role combines deep technical modeling expertise with infrastructure engineering to design, build, and operate end-to-end ML/AI systems at scale. You'll implement foundational MLOps frameworks across the full product lifecycle including data ingestion, ML processing, and results delivery/activation. Working cross-functionally with data science, data engineering, and architecture teams, you'll serve as both solutions architect and hands-on implementation engineer. Model Development & Optimization • Design and optimize machine learning models including deep learning architectures, LLMs, and specialized models (BERT-based classifiers) • Implement distributed training workflows using PyTorch and other frameworks • Fine-tune large language models and optimize inference performance using compilation tools (Neuron compiler, ONNX, vLLM) • Optimize models for hardware targets (GPU, TPU, AWS Inferentia/Trainium) Infrastructure Design & AI-Services Architecture • Design AI-services and architectures for real-time streaming and offline batch optimization use-cases • Lead ML infrastructure implementation including data ingestion pipelines, feature processing, model training, and serving environments • Build scalable inference systems for real-time and batch predictions • Deploy models across compute environments (EC2, EKS, SageMaker, specialized inference chips) MLOps Platform & Pipeline Automation • Implement and maintain MLOps platform including Feature Store, ML Observability, ML Governance, Training and Deployment pipelines • Create automated workflows for model training, evaluation, and deployment using infrastructure-as-code • Build MLOps tooling that abstracts complex engineering tasks for data science teams • Implement CI/CD pipelines for model artifacts and infrastructure components Performance & Cross-functional Partnership • Monitor and optimize ML systems for performance, accuracy, latency, and cost • Conduct performance profiling and implement observability solutions across the ML stack • Partner with data engineering to ensure optimal data delivery format/cadence • Collaborate with data architecture, governance, and security teams to meet required standards • Provide technical guidance on modeling techniques and infrastructure best practices