Staff AI/ML Engineer

What You Will Do: Partner with stakeholders to define and deliver AI/analytics use cases, translating business needs into scalable data science solutions. Design and develop machine learning models and analytical approaches to support search, discovery, and insight generation across structured and unstructured data. Build and implement NLP, semantic search, and entity resolution capabilities to enable advanced information retrieval and relationship analysis. Leverage document-based data (e.g., OCR/ICR outputs, metadata, and free text) to extract insights and support downstream analytics and search solutions. Collaborate with data engineers to integrate models into production environments, including Palantir Foundry, Databricks, and AWS-based platforms. Develop model evaluation frameworks, confidence scoring, and explainability approaches to ensure transparency and usability of AI outputs. Support the development of analytics, reporting, and dashboards to drive operational insights and decision-making. Operate within an Agile delivery model, contributing to sprint planning, experimentation, and iterative solution delivery. Communicate findings and recommendations clearly to both technical and non-technical audiences, including client stakeholders. Contribute to solution design, proposal support, and thought leadership in AI/analytics capabilities What You Will Need: A Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field is required A minimum of EIGHT (8) years of experience in data science, machine learning, or applied analytics roles Experience developing and applying machine learning models, including: Natural Language Processing (NLP) Semantic search or information retrieval Entity resolution or relationship modeling Experience working with large-scale structured and unstructured data, particularly document-based datasets (e.g., text, PDFs, images). Experience leveraging metadata and extracted features to support analytics and modeling. Strong proficiency in Python for data science and machine learning (e.g., Pandas, Scikit-learn, PyTorch or TensorFlow) and solid SQL skills. Experience working with Databricks and/or Spark-based environments for scalable data processing. Familiarity with AWS cloud services for data access, processing, or model deployment. Experience working with data lake or lakehouse architectures (e.g., AWS S3, Databricks), including querying and transforming large-scale datasets. Experience integrating models into production environments (e.g., APIs, batch pipelines, or embedded analytics platforms). Understanding of model evaluation, validation, and performance metrics. Strong communication skills and ability to translate analytical outputs into actionable insights. Experience working in cross-functional, matrixed teams in an Agile environment. What Would Be Nice To Have: Experience supporting fraud detection, risk analytics, or investigative use cases, including development of models or analytics to identify anomalous behavior, patterns, or suspicious activity. Familiarity with law enforcement, regulatory, or compliance-focused analytics environments, including data used in investigations, intelligence analysis, or case management workflows. Experience working with abuse-related datasets or use cases (e.g., financial fraud, identity theft, program integrity, cyber abuse), including applying NLP, entity resolution, or graph analytics to detect and analyze abusive or fraudulent behavior. Understanding of investigative analytics techniques, including link analysis, network/graph-based modeling, and relationship mapping to support law enforcement or fraud detection initiatives. Experience working with Palantir Foundry and/or Palantir AIP, particularly in support of AI-enabled search or analytics workflows. Consulting experience strongly preferred. Experience building AI-enabled search solutions, including semantic search, document retrieval, and ranking models. Experience with multimodal data processing, including text and image-based analytics. Familiarity with OCR/ICR pipelines and document intelligence use cases. Experience with enterprise ML platforms (e.g., AWS SageMaker, Databricks Machine Learning) for model development, deployment, and lifecycle management. Experience developing explainable AI (XAI) solutions, including confidence scoring and traceability of results. Experience designing analytics dashboards or reporting solutions for end users. Previous experience supporting federal clients or working in regulated environments. Experience in a consulting firm and/or client-facing delivery role. Experience supporting training, user enablement, or scaling analytics capabilities across teams. Familiarity with graph-based analytics, ontology-driven models, or relationship mapping.