Booz Allen
ArtificialIntelligenceandMachineLearningEngineer,Mid
Neural analysis suggests this role is
optimal for Mid candidates.
“Artificial Intelligence and Machine Learning Engineer, Mid at Booz Allen. Skills: AI, ML, MLOps, Python, Databricks, Palantir, Amazon Bedrock. develop and operationalize secure, scalable, production-grade AI solutions. modernize and operate an end-to-end, AI-driven platform”
What You'll Achieve.
deliver high-impact AI and ML solutions across a broad range of use cases; sustain and advance mission-critical capabilities
Industry & Context.
U. S. citizenship is required, Ability to obtain and maintain a Public Trust or Suitability/Fitness determination, subject to a government investigation, identity verification process that leverages advanced biometrics and artificial intelligence, expected to be on camera during interviews and assessments, use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided, employees working virtually are generally expected to have their cameras on during meetings
What They're Looking For.
Must Have
Experience building, deploying, and operating production ML models such as supervised, unsupervised, and anomaly detection, including techniques for imbalanced datasets, Experience with ML engineering and MLOps, including model versioning, CI/CD for ML, monitoring, drift detection, and automated retraining, Experience with Python and ML frameworks such as scikit-learn, PyTorch, or TensorFlow, Experience with Palantir and data engineering platforms such as Databricks, Spark, or SQL, and batch and streaming pipelines, Experience improving data quality, lineage, and observability in enterprise data environments and operationalizing rules and model-driven scoring for prioritization, routing, or case selection, Experience with API-first and event-driven integration patterns, including secure service-to-service communication, Knowledge of responsible AI practices, including explainability, fairness, and bias assessment, Ability to design and document architecture artifacts, data contracts, and operational runbooks, Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements, 2+ years of experience with DevOps, software, or data engineering, or 5+ years of experience with DevOps, software, or data engineering in lieu of a degree
Nice to Have
Experience working in Agile delivery environments, collaborating with product owners, SMEs, and engineering teams, Experience with fraud detection, risk analytics, or case selection in government, tax, or financial domains, Experience with Amazon Bedrock and integrating custom AI models into enterprise workflows, Experience deploying ML solutions in AWS GovCloud or other regulated cloud environments, Experience with federal ATO processes, continuous compliance, and operating systems under FISMA controls, Experience in enterprise modernization programs such as cloud migration, microservices, API strategy, and DevSecOps, Knowledge of graph-based analytics and advanced anomaly detection techniques
What You'll Do.
develop and operationalize secure
production-grade AI solutions
modernize and operate an end-to-end
sustain and enhance batch and streaming data pipelines
define data contracts and feature pipelines
modernize legacy case selection capabilities by decomposing them into scalable services
operationalize rules and model-driven scoring
and human-in-the-loop review
build and operate production-grade ML pipelines with MLOps practices
integrate with shared enterprise services using API-first and event-driven patterns
harden the platform to meet security and compliance requirements
produce architecture and operational documentation
How You'll Work.
Team & Collaboration
collaborating with data engineers, data scientists, solution architects, and product owners; partner with data engineers and subject matter experts (SMEs); collaborate closely with product, fraud, and case management teams; collaborating with product owners, SMEs, and engineering teams
Process & Methodology
Agile delivery environment
Full Job Description
Artificial Intelligence and Machine Learning Engineer, Mid **The Opportunity:** As an experienced AI and ML engineer, you will help develop and operationalize secure, scalable, production-grade AI solutions that sustain and advance mission-critical capabilities. You will work as part of a cross-functional team, collaborating with data engineers, data scientists, solution architects, and product owners to deliver high-impact AI and ML solutions across a broad range of use cases. In this role, you will modernize and operate an end-to-end, AI-driven platform built on Databricks, Palantir, Amazon Bedrock, and custom AI and ML models. You will sustain and enhance batch and streaming data pipelines, improve data quality, lineage, and observability, and partner with data engineers and subject matter experts (SMEs) to define data contracts and feature pipelines. You will modernize legacy case selection capabilities by decomposing them into scalable services and operationalizing rules and model-driven scoring, prioritization, routing, and human-in-the-loop review. You will build and operate production-grade ML pipelines with strong MLOps practices, including versioning, CI/CD, monitoring, drift detection, explainability, and fairness, and integrate with shared enterprise services using API-first and event-driven patterns. You will also harden the platform to meet security and compliance requirements, including ATO, produce architecture and operational documentation, and collaborate closely with product, fraud, and case management teams in an Agile delivery environment. Due to the nature of work performed within this facility, U.S. citizenship is required. Join us. The world can’t wait. **You Have:** * Experience building, deploying, and operating production ML models such as supervised, unsupervised, and anomaly detection, including techniques for imbalanced datasets * Experience with ML engineering and MLOps, including model versioning, CI/CD for ML, monitoring, drift detec
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