Note: The job is a remote job and is open to candidates in USA. Motion Recruitment is recruiting for an unnamed industrial technology company. The Machine Learning Engineer will design, architect, and deploy scalable production machine-learning pipelines and predictive-maintenance systems, while supporting enterprise operational performance.
Responsibilities
- Lead the end-to-end lifecycle of production-grade machine learning models, from exploratory analysis and feature engineering to deployment, performance tracking, and continuous monitoring
- Architect and manage robust MLOps workflows and CI/CD pipelines across Databricks and AWS platforms, incorporating automated retraining schedules, model version control, and rollback mechanisms
- Build, evaluate, and scale traditional supervised, unsupervised, and time-series models (including regression, tree-based algorithms, SVMs, and clustering) to solve complex business and operational problems
- Apply rigorous statistical methods, hypothesis testing, and Bayesian techniques to ensure production models are accurate, reliable, and explainable
- Design, configure, and maintain diverse database architectures across SQL and NoSQL technologies to support real-time and batch machine learning workflows
- Collaborate with cross-functional engineering and software teams to integrate production models and data pipelines smoothly into backend services
Skills
- Master's or PhD degree in Computer Science, Data Science, Statistics, or a related quantitative field
- 8+ years of applied machine learning and data science experience, with at least 3 years operating at a senior or staff level
- At least 5 years of professional software development experience, primarily utilizing Python
- 2 to 3 years of hands-on data architect experience working within Databricks
- Demonstrated success deploying and maintaining traditional machine learning systems in production environments
- High proficiency with standard ML frameworks and statistical libraries such as Scikit-learn, XGBoost, LightGBM, Pandas, NumPy, and Statsmodels
- Strong practical background in MLOps tools such as MLflow, Databricks, Kubeflow, Vertex AI Pipelines, or AWS SageMaker Pipelines
- In-depth expertise in querying, modeling, and configuring relational (SQL) and non-relational (NoSQL) databases
- All applicants must be currently authorized to work on a full-time basis in the country for which they are applying, and no sponsorship is currently available
- This position cannot offer C2C or the transfer/sponsorship of any visas
- Experience analyzing manufacturing, industrial sensor, or PLC telemetry data for predictive maintenance applications
- Working knowledge or hands-on implementation of retrieval-augmented generation (RAG) pipelines and vector databases
- Demonstrated background in advanced time-series analysis and unsupervised anomaly detection on unlabeled datasets
- Multi-cloud architecture and deployment experience across AWS, Azure, or GCP
- Familiarity with distributed data processing systems such as Apache Spark or Ray
- Experience building or integrating backend microservices using Golang
Benefits
- 100% remote schedule
- Medical Insurance
- Dental & Orthodontia Benefits
- Vision Benefits
- Voluntary Life Insurance
- Long-Term & Short-Term Disability Insurance
- Hospital Indemnity Insurance
- Legal and Identity Protection Plans
- Pre-tax Commuter Benefit
- 529 College Saver Plan
Company Overview