About the job
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.
Responsibilities
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, and 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, and 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, and collaborate closely with product, fraud, and case management teams in an Agile delivery environment.
Qualifications
Minimum
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
Preferred
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