Booz Allen Hamilton is seeking an experienced AI and ML engineer to develop and operationalize secure, scalable AI solutions. The role involves collaborating with cross-functional teams to deliver impactful AI and ML solutions and modernizing an AI-driven platform.
Responsibilities:
- Help develop and operationalize secure, scalable, production-grade AI solutions that sustain and advance mission-critical capabilities
- 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
- Modernize and operate an end-to-end, AI-driven platform built on Databricks, Palantir, Amazon Bedrock, and custom AI and ML models
- Sustain and enhance batch and streaming data pipelines
- Improve data quality, lineage, and observability
- Partner with data engineers and subject matter experts (SMEs) to define data contracts and feature pipelines
- 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
- Build and operate production-grade ML pipelines with strong MLOps practices, including versioning, CI/CD, monitoring, drift detection, explainability, and fairness
- Integrate with shared enterprise services using API-first and event-driven patterns
- Harden the platform to meet security and compliance requirements, including ATO
- Produce architecture and operational documentation
- Collaborate closely with product, fraud, and case management teams in an Agile delivery environment
Requirements:
- 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
- Bachelor's degree and 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
- 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
- AWS Machine Learning Specialty, Security+, AI Engineer, or similar Certification