Machine Learning Engineer

The posting, as Accenture Federal Services wrote it

At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations.

Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more.

Join us to drive positive, lasting change that moves missions and the government forward!

At Accenture Federal Services (AFS), we use machine learning to solve high‑impact, mission‑critical problems across defense, national security, public safety, and federal civilian sectors. Our 13,000+ colleagues deliver production‑grade AI systems where reliability, traceability, and responsible use are non‑negotiable.

As part of our ML Engineering community, you’ll design, build, and deploy models that operate at federal scale improving outcomes for millions of people. You’ll grow through hands‑on delivery, certifications, and access to the latest tooling and training. We ship in weeks, not quarters, and we measure success through model performance, reliability, safety, and cost efficiency.

Confidentiality matters: We don’t share mission or customer specifics in public postings. If you advance, we’ll provide program‑level details as appropriate during the interview process.

The work

• 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. And the last one.
• Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications while monitoring performance and addressing model drift.

Here’s what you need

• Hands-on experience building, training, deploying, and maintaining machine learning models in production environments.
• Strong proficiency in Python and 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 tr

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ions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.

If you are being considered for employment opportunities with Accenture Federal Services and need an accommodation for a disability or religious observance during the interview process or for the job you are interviewing for, please speak with your recruiter.

Other Employment Statements

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.

Candidates who are currently employed by a client of Accenture Federal Services or an affiliated Accenture business may not be eligible for consideration.

Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process.

The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.

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