Machine Learning Engineer

Summary

Join Blue Orange Digital, a cloud-based data transformation and predictive analytics firm, as a Machine Learning Engineer. You will design, build, and deploy advanced machine learning models, improve model performance, and build LLM-based products. The role requires experience with various ML frameworks, cloud technologies, and MLOps practices. You will collaborate with technical and non-technical stakeholders. This position offers a fully remote work environment with a flexible schedule and unlimited PTO. Blue Orange provides opportunities to work on cutting-edge projects and grow professionally within a top-notch team.

Requirements

• 1 - 3 years experience practicing ML/AI data engineering
• Degree in Computer Science, Engineering, Mathematics, or a related field
• Strong mathematical skills, particularly in statistics and linear algebra
• Experience with NLP and LLM-based technologies and frameworks
• Proficiency in programming languages such as Python
• Experience with cloud-based technologies AWS, GCP, and/or Azure
• Expertise in training and deploying ML/AI-powered solutions in cloud environments

Responsibilities

• Develop and Implement Machine Learning and AI Models: Design, build, and deploy advanced machine learning models
• Improve model performance by conducting feature engineering, hyperparameter search, and metric selection
• Build LLM-based products and stay up to date with current developments. Proficiency using Hugging Face, OpenAI, Anthropic, and/or Cohere tools
• Design and build custom APIs with tools like FastAPI
• Build LLM orchestration systems with tools like LangChain, LLamaIndex, Semantic Kernel, and/or HayStack
• Build predictive analytics and modeling products using tools like Sklearn, Sktime, XGboosts, and/or LightGBM
• Data Analytics and Processing: Analyze large, complex datasets to extract actionable insights and inform model development
• Implement data preprocessing, cleansing, and quality checks to ensure data quality
• Cloud-Native Solutions and MLOps: Develop and maintain cloud-native machine learning solutions using any of the major clouds: AWS (Lambda, EMR, GLUE, ECS, EKS), GCP (GKE, Anthos, Cloud Run), and/or Azure (CA, KS)
• Implement and manage MLOps practices to automate and streamline the ML model deployment process. Using tools such as MLflow and/or Weights and Biases for storing metrics, artifacts, and experiments
• Containerization Technologies: Utilize containerization technologies like Docker and Docker-compose to ensure consistent and scalable deployment of machine learning models. Using FastAPI microservices
• Quality Assurance and Best Practices: Ensure the highest quality of machine learning models through rigorous testing and validation. Using unit and integration testing with CI/CD pipelines through GitHub actions
• Advocate and adhere to best software (i.e., SOLID, DRY, Git version control, etc.) and machine learning (train, val, test data splits, baseline definition, overfitting management, etc) within the team

Preferred Qualifications

• Advanced degree in a relevant field
• Publications in relevant AI/ML communities and journals
• Deep Learning Expertise in Tensorflow and/or Pytorch
• Experience Fine-tuning OpenSource LLMs and deploying them
• Great Expectations and/or DBT is a plus

Benefits

• Fully remote
• Flexible Schedule
• Unlimited Paid Time Off (PTO)
• Paid parental/bereavement leave
• Worldwide recognized clients to build skills for an excellent resume
• Top-notch team to learn and grow with