- Company Name
- Focus Financial Partners
- Job Title
- Machine Learning Engineer
- Job Description
-
**Job Title:**
Machine Learning Engineer
**Role Summary:**
Design, deploy, and maintain production‑grade machine learning systems for real‑world applications. Work across the end‑to‑end ML lifecycle—data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement—collaborating with data scientists, software engineers, and product teams.
**Expectations:**
- 3+ years of hands‑on machine learning engineering or applied ML experience.
- Master’s degree in Computer Science, Data Science, Engineering, or related field.
- Proficiency in Python and modern ML frameworks (TensorFlow, PyTorch, scikit‑learn).
- Experience with distributed data processing (Pandas, Spark, Dask).
- Containerization (Docker) and orchestration (Kubernetes).
- Familiarity with CI/CD, Git, and testing automation.
- Cloud platform knowledge (AWS, Azure, GCP) and managed ML services (SageMaker, Vertex AI, Databricks, Snowflake ML).
- Strong understanding of model evaluation, feature engineering, and production performance optimization.
**Key Responsibilities:**
- Develop, deploy, and optimize ML models for business use cases.
- Operationalize predictive models with scalable, maintainable production deployments.
- Design and implement data pipelines for training, inference, and model lifecycle management.
- Ensure data quality, reproducibility, and version control across ML workflows.
- Implement monitoring, logging, and alerting for model performance and drift.
- Leverage cloud services and infrastructure‑as‑code for scalable solutions.
- Write clean, modular, well‑documented code following MLOps and software engineering best practices.
- Keep abreast of emerging ML tools and industry best practices to enhance platform capabilities.
**Required Skills:**
- Python programming
- TensorFlow / PyTorch / scikit‑learn
- Pandas / Spark / Dask
- Docker & Kubernetes
- CI/CD pipelines, Git, automated testing
- Cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML, Databricks, Snowflake ML)
- Data pipeline design
- Model monitoring, drift detection, retraining workflows
- Feature engineering, model evaluation, performance tuning
- Strong analytical, communication, and collaboration skills
**Required Education & Certifications:**
- Master’s degree in Computer Science, Data Science, Engineering, or related technical field.
- (Optional) Certifications in cloud ML services (e.g., AWS Certified Machine Learning – Specialty, GCP Professional Data Engineer).