Hybrid ML Engineer

at NextGen IT Services

• Job title: ML Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: No Education Requirement
• Tech skills: AWS, EC2, PySpark, Python, PyTorch, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Washington, D C, United States

About the role

• Machine Learning Engineer designing, building, and operationalizing AI/ML solutions for mission-critical applications. Collaborating with data engineering teams to support production-grade ML systems in a hybrid work environment.

Responsibilities

• 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.
• Implement and optimize Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and AI agent architectures for enterprise use cases.
• 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.
• 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.
• Utilize AWS services such as S3, EC2, Lambda, SageMaker, and Step Functions for scalable ML workloads.
• 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.

Requirements

• 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.

Benefits

• Medical
• Dental
• Vision
• 401k with match
• Flexible Spending Account
• Paid Time Off (PTO)—including vacation and holiday pay