Job Overview
We are seeking an experienced Machine Learning Engineer / MLOps Engineer to design, build, and optimize scalable AI and machine learning solutions. The ideal candidate will possess deep technical expertise in data pipeline development, MLOps automation, and cloud-based model deployment (preferably AWS SageMaker), working closely with data science and engineering teams to ensure reliability, efficiency, and operational excellence in production ML systems.
Key Responsibilities
- Design, develop, and optimize complex data and model pipelines using machine learning engineering best practices for scalability, efficiency, and reliability.
- Develop and implement robust MLOps pipelines to automate model training, deployment, monitoring, and lifecycle management in production.
- Integrate, monitor, and maintain data and model pipelines, proactively identifying data quality issues and documenting assumptions.
- Collaborate with data scientists to validate model-ready datasets, ensuring accuracy and complete feature documentation.
- Conduct exploratory data analysis (EDA) and data discovery on raw data sources, incorporating business context to improve model performance.
- Track data lineage and perform root-cause analysis during data exploration or issue resolution.
- Partner with business and technical stakeholders to translate complex business processes into scalable, analytical ML solutions.
- Develop and maintain model monitoring scripts, investigate model drift and data anomalies, and coordinate timely issue resolution.
Required Skills & Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, or a related field (Master’s preferred).
- 8+ years of experience in AI Engineering, Machine Learning Engineering, or Data Engineering.
- 5+ years of hands-on experience building ETL and ML pipelines using AWS services (Glue, Lambda, SageMaker, S3, Step Functions, etc.).
- Proven expertise in MLOps implementation for deploying, monitoring, and managing ML models in production environments.
- Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
- Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker.
- Solid grasp of software engineering principles including design patterns, testing, CI/CD, security, and version control (Git).
- Knowledge of the Machine Learning Development Lifecycle (MDLC) and industry AI engineering best practices.
- Experience designing and implementing end-to-end ML solution architectures integrating data ingestion, feature engineering, model training, and monitoring.
Preferred Skills
- Familiarity with containerization and orchestration tools (Docker, Kubernetes, EKS).
- Experience with feature stores, data versioning, and model registries.
- Exposure to monitoring and observability tools (Prometheus, Grafana, CloudWatch) for ML systems.
- Strong understanding of data governance, model explainability, and ethical AI principles.
We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law