Machine Learning Engineer

Description

The company uses machine learning on high-impact, mission-critical problems for US federal clients in defense, national security, public safety, civilian work, and military health.

It is a technology company within the wider Accenture group with more than 13,000 staff.

The team ships production AI systems and treats reliability, traceability, and responsible use as requirements, not options.

Success is judged on model performance, reliability, safety, and cost efficiency, and delivery typically happens in weeks rather than quarters.

Mission and customer details are kept out of public postings and are shared with shortlisted candidates during interviews as appropriate.

Responsibilities

• Create MLOps frameworks and workflows that fit different domains and applications.
• Build, train, deploy, and keep machine learning models running in production.
• Design, build, and maintain end-to-end ML pipelines covering data ingestion, feature engineering, training, validation, deployment, and monitoring.
• Set up MLOps practices such as CI/CD pipelines, model versioning, model registries, feature stores, and automated retraining.
• Deploy, monitor, and tune ML solutions on cloud platforms and containerized tools such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML.
• Work with engineering and data teams to bring scalable ML into mission-critical applications, watching performance and handling model drift.

Requirements

• Hands-on experience building, training, deploying, and maintaining machine learning models in production.
• Strong Python skills and experience with at least one framework such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or Hugging Face.
• Experience building and maintaining end-to-end ML pipelines, from data ingestion through to monitoring.
• Experience with MLOps tools and practices, including model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining.
• Experience deploying models with cloud native or containerized tools such as Docker, Kubernetes, Amazon SageMaker, Google Vertex AI, or Azure Machine Learning.
• Experience monitoring live ML systems, diagnosing performance problems, and handling model drift.
• Must be a U.S. citizen with no dual citizenship.
• Bonus: an advanced degree in computer science, technology, engineering, or mathematics (STEM); a Ph.D. is preferred but not required.

Conditions

Base pay range of $108,400 - $203,400 USD applies to roles in California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia.

Pay depends on office location, role, skill set, and experience level.

Applications are accepted on an ongoing basis with no fixed deadline.

Equal opportunity is a core commitment, and all qualified candidates are considered.

Benefits

• A wide range of benefits is offered, with details available on the company's benefits page.
• Hands-on delivery, certifications, industry training, and access to current tooling support your growth.
• A collaborative, caring team where you can feel you belong.