AI/ML Engineer

🕒 July 30

🏛️ District of Columbia, Washington – Remote

🏛️ District of Columbia – Remote ☕ Washington – Remote

💵 $108.4k - $203.4k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 33%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Accenture Federal Services

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 2017

💼 Consulting

🎖️ Defense

📦 Logistics

Consulting • Defense • Logistics

Accenture Federal Services is a division of Accenture that focuses on delivering technology and consulting services to the U. S. federal government. With capabilities in cloud, cybersecurity, data and artificial intelligence, digital engineering, and emerging technologies, Accenture Federal Services supports federal agencies in achieving mission-critical outcomes through innovation and technological advancement. The company emphasizes building secure, scalable solutions, enhancing decision-making with data-driven insights, and transforming digital infrastructures to meet complex governmental needs. By leveraging advanced R&D and human-centered design, they aim to improve the performance and resilience of government operations.

📋 Description

• Develop MLOps frameworks and workflows for a variety of domains and applications • Build, train, deploy, and maintain machine learning models in production environments • Design, develop, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, deployment, and monitoring • Implement MLOps frameworks and best practices, including CI/CD pipelines, model versioning, model registries, feature stores, and automated retraining workflows • Deploy, monitor, and optimize machine learning solutions using cloud platforms and containerized technologies such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML • Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications • Monitor performance and address model drift

🎯 Requirements

• Hands-on experience building, training, deploying, and maintaining machine learning models in production environments • Strong proficiency in Python • Experience with one or more machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or Hugging Face • Experience developing and maintaining end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring • Experience with MLOps practices and tools, including model versioning, CI/CD pipelines, model registries, feature stores, model monitoring, and automated retraining workflows • Experience deploying machine learning models using cloud native or containerized technologies such as Docker, Kubernetes, Amazon SageMaker, Google Vertex AI, or Azure Machine Learning • Experience monitoring production machine learning systems, troubleshooting model performance issues, and addressing model drift • Must be a U.S. Citizen (No Dual citizenship) • Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States

🏖️ Benefits

• Collaborative and caring community • Hands-on experience • Certifications • Industry training • Equal employment opportunities • Reasonable accommodations for disabilities or religious observances • Veteran employment opportunities