Staff AI/ML Engineer & Data Scientist

Location: Fully Remote or Onsite (Normal, IL)

Duration: 12-Month Contract

Schedule: Monday–Friday | 9:00 AM – 6:00 PM CT (1-hour unpaid lunch)

Pay Range:$67.50–$72.50/hour

Role Summary

We are seeking a Staff AI/ML Engineer & Data Scientist with deep expertise in traditional machine learning, deep learning, and strong MLOps experience to lead the design, deployment, and maintenance of production-grade ML systems.

This role will architect robust ML pipelines, apply advanced statistical techniques, and ensure models are accurate, explainable, and scalable. The primary focus will be on supervised, unsupervised, and time-series modeling. Light exposure to retrieval-augmented generation (RAG) is a plus.

Strong DevOps experience is required, including database setup, CI/CD pipelines, and end-to-end Databricks implementation.

This role will support manufacturing-related initiatives, including predictive maintenance projects leveraging sensor and PLC data.

Most Critical Skills & Experience

• Strong Databricks AI/ML and Databricks MLOps expertise
• AWS MLOps and software development experience
• DevOps experience (Databases, CI/CD, MLOps, VectorDBs, GraphDBs)
• At least 5 years of software development experience (required)
• 2–3 years of Data Architect experience within Databricks (required)
• Experience working with manufacturing, sensor, or PLC data (highly preferred)

Key Responsibilities

ML Technical Leadership

• Define ML architecture, best practices, and performance standards for enterprise-scale solutions

End-to-End Model Development

• Lead full lifecycle development: data preprocessing, feature engineering, model training, validation, deployment, and monitoring

Traditional ML Expertise

• Apply regression, tree-based models, SVMs, clustering, forecasting, and anomaly detection
• Perform feature engineering and hyperparameter tuning

Programming & Integration

• Build scalable ML pipelines and APIs using Python (primary)
• Support backend services using Golang (preferred)

MLOps Implementation

• Design and manage CI/CD pipelines for ML
• Implement automated retraining, model versioning, monitoring, and rollback strategies

Statistical Analysis

• Apply hypothesis testing, Bayesian methods, and model interpretability techniques

Cross-Functional Collaboration

• Partner with engineering, analytics, and product teams to align technical solutions with business objectives

DevOps

• Database setup and optimization
• CI/CD pipeline implementation
• Cloud infrastructure support (AWS preferred)
• Experience with VectorDBs and GraphDBs

Required Qualifications

• 8+ years of experience in applied ML or data science
• 3+ years in a senior or staff-level role
• 5+ years of software development experience (mandatory)
• 2–3 years of Data Architect experience in Databricks (mandatory)
• Master’s degree or PhD in Computer Science (mandatory)
• Expert proficiency in Python
• Proven production deployment of traditional ML models with measurable impact
• Strong knowledge of Scikit-learn, XGBoost, LightGBM
• Experience with Pandas, NumPy, Statsmodels
• Hands-on MLOps experience with tools such as:
  • MLflow (preferred)
  • Databricks (preferred)
  • Kubeflow
  • Vertex AI Pipelines
  • AWS SageMaker Pipelines
• Experience with model monitoring, drift detection, and automated retraining
• Strong SQL and NoSQL database skills

Preferred Qualifications

• Exposure to RAG pipelines and vector databases
• Time-series modeling and anomaly detection expertise
• Cloud deployment experience (AWS, Azure, GCP)
• Familiarity with distributed computing frameworks (Spark, Ray)

Soft Skills

• Strategic problem-solver with ability to align AI solutions to business objectives
• Strong communicator across technical and non-technical stakeholders
• Comfortable operating in enterprise-scale environments

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