_{25th February, 2026}

A leading technology innovator is seeking an experienced Machine Learning (ML) Engineer to join our dynamic team. In this permanent, full-time role, you'll have the opportunity to design, build, and operationalize scalable AI/ML solutions that power mission-critical applications across our organization.
 
With a competitive salary, this position offers a comprehensive benefits package, including medical, dental, vision, 401(k) with matching, and generous paid time off.
 
Unlock Your Potential as an ML Engineer:

• Collaborate with data scientists and subject matter experts to develop cutting-edge machine learning models using curated datasets.
• Implement and optimize Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and AI agent architectures for enterprise-grade solutions.
• Operationalize models through robust CI/CD workflows, leveraging MLflow, AWS SageMaker, and custom APIs to deploy ML solutions at scale.

Model Development

• Collaborate with data scientists and subject matter experts to develop machine learning models using curated datasets.
• Conduct experiments, prototypes, and proofs-of-concept 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

Requirements:

• Hands-on experience with major ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow) and proficiency in Python programming.
• Expertise in Databricks, MLflow, and PySpark for building scalable, reusable ML training pipelines.
• Solid understanding of the end-to-end model lifecycle and MLOps best practices, including model monitoring, retraining, and governance.
• Experience with AWS-based data infrastructure and DevOps workflows, including services like S3, EC2, Lambda, SageMaker, and Step Functions.
• Proven ability to productionize ML models and integrate them into business systems, with a focus on performance, accuracy, and latency.

Preferred Qualifications:

• 5+ years of experience in ML Engineering or Applied Machine Learning, with a strong background in software engineering principles and best practices.
• Strong Python programming skills.
• 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.
• Experience building rapid-prototype AI model interfaces with Streamlit, Gradio, or similar tools is preferred.
• Business acumen with the ability to align ML solutions with organizational goals is preferred.
• Experience optimizing compute and storage resources for performance and cost efficiency is preferred.

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 are preferred.

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