Our client is seeking an experienced Machine Learning (ML) Engineer to design, build, and operationalize scalable AI/ML solutions across a variety of mission-critical applications. This role combines hands-on model development, MLOps engineering, cloud-native deployment, and close collaboration with data engineering teams to support modern, production-grade ML systems. The ideal candidate will bring deep expertise in Python, ML frameworks, Databricks, MLflow, AWS, and emerging AI technologies such as LLMs, RAGs, and AI agent frameworks.

This is a direct-hire, full-time position with salary and benefits. Our client provides a comprehensive benefits package, Medical, Dental, Vision, 401k with match, Flexible Spending Account, and Paid Time Off (PTO)—including vacation and holiday pay. 

Location: Hybrid in Washington, DC

Clearance: Must be a U.S. Citizen and be able to obtain a U.S. Federal government client badge and will be required to pass a government background investigation. Candidates with active DOT clearance preferred.

Responsibilities:

Model Development

• Collaborate with data scientists and subject matter experts to develop machine learning models using curated datasets.
• Conduct experiments, prototypes, and proof-of-concepts to validate and refine model performance.
• Build scalable, reusable training pipelines using Databricks notebooks and MLflow.

Implementation & Optimization

• Implement and optimize Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and AI agent architectures for enterprise use cases.

Deployment & MLOps

• Operationalize models using robust CI/CD workflows.
• Deploy ML solutions via MLflow, AWS SageMaker, or custom APIs.
• Monitor production performance for accuracy, drift, and latency; manage retraining cycles and model governance.

Data Integration & Architecture Alignment

• Partner with Data Engineering to align ML pipelines with the Bronze, Silver, and Gold layers of a Medallion Architecture.
• Engineer high-quality features and maintain training and inference pipelines.

Cloud & Platform Engineering

• Utilize AWS services such as S3, EC2, Lambda, SageMaker, and Step Functions for scalable ML workloads.

Collaboration & Documentation

• Document ML artifacts, processes, and performance outcomes clearly and comprehensively.
• Collaborate within agile teams, support project ceremonies, and maintain stakeholder communication.
• Mentor junior team members and share best practices.

Minimum Qualifications:

• 5+ years of experience in ML Engineering or Applied Machine Learning.
• Strong Python programming skills.
• Hands-on experience with major ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
• Proficiency with Databricks, MLflow, and PySpark.
• Solid understanding of the end-to-end model lifecycle and MLOps best practices.
• Experience with AWS-based data infrastructure and DevOps workflows.
• Proven ability to productionize ML models and integrate them into business systems.
• Strong understanding of mathematics and statistics relevant to ML and AI.
• Experience with supervised, unsupervised, and deep learning techniques.
• Solid background in software engineering principles and best practices.
• Hands-on experience with training frameworks such as TensorFlow, PyTorch, or Hugging Face.
• Practical experience building and deploying LLMs, RAGs, and AI agent systems.
• Demonstrated expertise with Databricks for data engineering and ML pipeline development.
• Excellent communication and teamwork skills.

Preferred Qualifications:

• Experience building rapid-prototype AI model interfaces with Streamlit, Gradio, or similar tools.
• Business acumen with the ability to align ML solutions with organizational goals.
• Experience optimizing compute and storage resources for performance and cost efficiency.