Senior Development Operations Engineer

Staff AI/ML Engineer & Data Scientist

Schedule: 9-6 Central Time (1 hour non-billable lunch) M-F

THIS WILL BE MOSTLY REMOTE BUT there will be 1 trip (you can expense for) to Normal each month for first 3 months. The trip will be 3 days working in Normal.

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. You will architect robust ML pipelines apply advanced statistical techniques and ensure models are accurate explainable and scalable. While the primary focus will be on traditional supervised unsupervised and time-series modeling light experience with retrieval-augmented generation (RAG) is a plus. The individual needs to have devops experience for setting up Databases CI/CD (Databricks end-to-end experience is plus)

MOST IMPORTANT SKILLS/RESPONSIBILITIES:

-strong databricks MLOPS databricks AI/ML and aws MLOPS and software experience

• Traditional ML Expertise Apply algorithms such as regression tree-based models SVMs clustering and forecasting to solve high-impact problems feature engineering and hyper parameter tuning (anamoly prediction). The vast majority of data generated today is unlabeled
  

• End-to-End Model Development Lead the full lifecycle from data preprocessing and feature engineering to training validation deployment and monitoring.
  

• Statistical Analysis Apply hypothesis testing Bayesian methods and model interpretability techniques to ensure reliable insights.
  Devops Experience Experience with database setup databricks aws CI/CD DevOps/MLOps VectorDBs GraphDB
  
  - Masters degree or PHD is mandatory
  - This role required visiting the site intermittently to the NormalIL site for initial understanding of the scope.
  - This role required analysis of manufacturing sensors PLC data any prior would be a plus
  

Key Responsibilities

• ML Technical Leadership Define ML architecture best practices and performance standards for enterprise-scale solutions.
  

• End-to-End Model Development Lead the full lifecycle from data preprocessing and feature engineering to training validation deployment and monitoring.
  

• Traditional ML Expertise Apply algorithms such as regression tree-based models SVMs clustering and forecasting to solve high-impact problems feature engineering and hyper parameter tuning.
  

• Programming & Integration Build scalable ML pipelines and APIs in Python (primary) and Golang (for backend services).
  

• MLOps Implementation Design and manage CI/CD pipelines for ML including automated retraining model versioning monitoring and rollback strategies.
  

• Statistical Analysis Apply hypothesis testing Bayesian methods and model interpretability techniques to ensure reliable insights.
  

• Cross-Functional Collaboration Partner with engineering analytics and product teams to align technical solutions with business objectives.
  Devops Experience Experience with database setup databricks aws CI/CD DevOps/MLOps vectorDBs GraphDB

Qualifications

Must Have:

• 8 years of experience in applied ML or data science including 3 years in a senior or staff-level role and devops experience.
  

• Expert proficiency in Python for ML development (Good to have: Golang for backend integration)
  

• Proven experience 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 like MLflow (preferred) Databricks(preferred) Kubeflow Vertex AI Pipelines or AWS SageMaker Pipelines.
  

• Experience with model monitoring drift detection and automated retraining strategies.
  

• Strong database skills (SQL and NoSQL).
  
  - Masters degree or PHD is mandatory
  

Preferred:

• Exposure to retrieval-augmented generation (RAG) pipelines and vector databases.
  

• Time-series analysis and anomaly detection experience.
  

• Cloud deployment expertise (AWS Azure GCP).
  

• Familiarity with distributed computing frameworks (Spark Ray).
  

Soft Skills

• Strategic problem-solver with the ability to align AI solutions to business goals.
  

• Excellent communicator across technical and non-technical stakeholders.
  

(Normal Illinois)