AI/ML Data Engineer

Precise Software Solutions Incorporated is hiring an AI/ML Data Engineer in Washington, DC for an onsite role supporting the U.S. Government. This position focuses on designing and governing the data foundation that enables secure AI-enabled applications, with end-to-end ownership of AI-ready datasets and measurable, secure AI operations. What You’ll Do Design and maintain data ingestion, transformation, and processing pipelines (ETL/ELT) for AI training, evaluation, retrieval, and operations, including support for data migration and cleansing. Curate, validate, and version datasets while maintaining dataset inventories, metadata, lineage, provenance, and ingestion logs. Implement automated data-quality checks for duplication, schema changes, completeness, and freshness; maintain dataset quality scorecards and drift reports. Design data models, vector stores, and embedding schemas for Retrieval-Augmented Generation (RAG) knowledge bases, including re-indexing content when sources change. Measure retrieval and model quality against established baselines using metrics such as precision/recall, MRR, NDCG, and context relevance. Prepare data-related deliverables, including AI model cards, ML and AI pipeline documentation, RAG/AI pipeline evaluation reports, data dictionaries, embedding schema documentation, and responses to Government data calls. Build secure structured-data access for AI applications such as natural-language-to-SQL with query validation and role-based authorization, and support dashboards and operational analytics. Monitor data and ML pipelines, troubleshoot failures, and support root-cause analysis while keeping data in FedRAMP-authorized cloud regions using FIPS-validated encryption. Support Responsible AI by preparing representative evaluation datasets, testing AI outputs for bias, accuracy, and hallucination, and documenting results to align with federal AI governance requirements. Secure the AI data path from source datasets and embeddings to prompts and logs, including support for AI risk testing such as data poisoning. Required Qualifications Bachelor’s degree in Computer Science, Engineering, Mathematics, Information Systems, or a related field and 5+ years of relevant experience; equivalent experience may substitute for the degree. 5+ years of hands-on data engineering or database development experience, including data modeling, SQL, and ETL/ELT pipeline development. Strong proficiency in Python and experience with data processing frameworks such as pandas and Spark, plus workflow orchestration tools such as Airflow. 1+ year preparing data for Generative AI or machine learning, including embeddings, vector databases, RAG knowledge bases, or training and evaluation datasets. Experience implementing data quality, lineage, metadata management, and data governance controls. Experience protecting sensitive data (PII), including masking, minimization, and access controls. Experience with a major cloud data platform (AWS, Azure, or Google Cloud), Git, and CI/CD tools. Must be a U.S. citizen or lawful permanent resident (green card holder). Must reside in the Washington, DC metropolitan area and be able to work onsite at Government offices. Must be able to obtain and maintain a Public Trust background investigation. Technology Stack Python, pandas, Spark, Airflow, AWS, Azure, Google Cloud, Git, CI/CD Vector stores, vector databases, pgvector, OpenSearch FIPS-validated encryption, FedRAMP-authorized cloud regions Natural-language-to-SQL Benefits Comprehensive Health Benefits (Medical, Dental and Vision) Flexible Spending Accounts (FSA) & Health Savings Account (HSA) Retirement Plan with 4% match and discretionary match at year end Paid Time Off (PTO): 15 days of PTO accrued per year; 7 holidays + 3 Floating holidays; 2 Innovation days (paid training days) Short Term and Long-Term Disability Paid Parental Leave Paid Jury Duty leave Life and AD&D Insurance Critical Illness Insurance Training and Development Wellness Incentives & Discount programs Employee Referral Program Annual Charity Donation Match Awards and Recognition Preferred Qualifications Master’s degree in a related field and 7+ years of data engineering experience, including support of federal agency programs. Experience evaluating retrieval quality and building RAG pipelines with vector stores (e.g., pgvector, OpenSearch). Experience with MLOps tooling, including model and dataset versioning, feature stores, or model registries. Generative AI, LLM security, or data certification (e.g., Databricks Generative AI Engineer, Snowflake SnowPro, AWS, Microsoft Azure, or Google Cloud). An active Public Trust or prior federal background investigation.

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