Staff AI/ML Engineer & Data Scientist
Normal, IL
Duration:
1 year
Schedule:
Standard Office Hours
Pay:$65-70/hr
Key Responsibilities
- Define architecture, best practices, and performance standards for enterprise-scale ML solutions.
- Lead all stages of the lifecycle—from data pre-processing and feature engineering to model training, validation, deployment, and monitoring.
- Apply algorithms such as regression, tree-based models, SVMs, clustering, and forecasting to high-impact problems, feature engineering and hyper parameter tuning.
- Develop scalable ML pipelines and APIs using Python, with backend support in Golang.
- Design and manage CI/CD pipelines for ML, including automated retraining, versioning, monitoring, and rollback.
- Utilize hypothesis testing, Bayesian methods, and model interpretability techniques to ensure reliable insights.
- Work closely with engineering, analytics, and product teams to align technical solutions with business objectives.
Required Qualifications
- Master’s degree or PhD in a relevant field.
- 8+ years of applied ML or data science experience, including 3+ years in a senior or staff-level role.
- Expert-level Python proficiency for ML development; Golang experience for backend integration.
- Proven track record deploying traditional ML models to production with measurable business impact.
- Strong knowledge of ML frameworks (Scikit-learn, XGBoost, LightGBM) and data libraries (Pandas, NumPy, Statsmodels).
- Hands-on MLOps experience with tools such as MLflow, Databricks MLFlow, Kubeflow, Vertex AI Pipelines, or AWS SageMaker Pipelines.
- Experience with model monitoring, drift detection, and automated retraining strategies.
- Strong database skills in both SQL and NoSQL environments.
- Strategic thinker able to align AI solutions with business goals.
- Effective communicator capable of engaging both technical and non-technical stakeholders.
Preferred Qualifications
- Experience with retrieval-augmented generation (RAG) pipelines and vector databases.
- Experience with MLflow and Databricks- MLFlow tools.
- Expertise in time-series analysis and anomaly detection.
- Cloud deployment experience (AWS, Azure, GCP).
- Familiarity with distributed computing frameworks (Spark, Ray).
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