"Alerted.org

Job Description

This global hotel chain 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

We are a company committed to creating inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity employer that believes everyone matters. Qualified candidates will receive consideration for employment opportunities without regard to race, religion, sex, age, marital status, national origin, sexual orientation, citizenship status, disability, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to Human Resources Request Form (https://airtable.com/app21VjYyxLDIX0ez/shrOg4IQS1J6dRiMo) . The EEOC "Know Your Rights" Poster is available here (https://www.eeoc.gov/sites/default/files/2023-06/22-088\_EEOC\_KnowYourRights6.12ScreenRdr.pdf) .

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Skills and Requirements

Master's degree in Computer Science, Software Engineering, Machine Learning, or related fields

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: AWS services (EC2, EKS, S3, SageMaker, Inferentia/Trainium), 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 Recommendation systems, NLP applications, or real-time inference systems experience

MLOps platform development and feature store implementations null

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to race, color, ethnicity, religion,sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military oruniformed service member status, or any other status or characteristic protected by applicable laws, regulations, andordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to [email protected].