Hybrid AI/ML Data Scientist - MLOps & Quantitative

AIToolboard; Washington (District of Columbia); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

W2 benefits

Medical, Dental, Vision

PTO 21 days

401k

Tuition reimbursement

Performance bonuses

Paid overtime

Job summary

Systems Engineering Services in Washington, DC is seeking a Senior AI/ML Engineer/Data Scientist with MLOps experience to build production-grade AI/ML solutions end-to-end. This role requires translating business problems into robust ML systems and delivering scalable, reliable models.

You will develop models, deploy pipelines, and collaborate with MLOps teams to monitor, version, and retrain models for production use. Strong software engineering and communication skills are essential.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or a related quantitative field.

Responsibilities

• Translate complex business requirements into AI/ML-based technical solutions and ensure efficiency, scalability and reliability.
• Design, develop, validate, and document AI/ML models and applications.
• Build production-grade Python code and pipelines for data processing, feature engineering, training, and inference.
• Develop model-driven applications and services (batch or real-time).
• Apply software engineering best practices including modular design, testing, code reviews, and CI/CD.
• Collaborate with MLOps teams on deployment, monitoring, versioning, and retraining.
• Implement model performance, stability, and data drift monitoring.
• Produce documentation to support governance, validation, and audit requirements.

Skills

Python

Statistical modeling

ML/AI platforms

MLOps

Data processing

Education

BSc/MSc in DS

Tools

TensorFlow

PySpark

SageMaker

MLflow

Redshift

Job description

Systems Engineering Services in Washington, DC is seeking a Senior AI/ML Engineer/Data Scientist with MLOps experience to build production-grade AI/ML solutions end-to-end. This role requires translating business problems into robust ML systems and delivering scalable, reliable models.

You will develop models, deploy pipelines, and collaborate with MLOps teams to monitor, version, and retrain models for production use. Strong software engineering and communication skills are essential.