Data & AI Engineer

APV is hiring a Data & AI Engineer to provide hands-on data engineering and applied AI/LLM support for a federal proof of concept focused on AI-assisted clinical trial protocol review. The role prepares protocol data, implements structured data components, configures and tests LLM-based extraction workflows, and supports traceable retrieval, comparison, and validation in approved federal environments.

Responsibilities

Implement and maintain protocol data structures and database components that support extraction, normalization, storage, retrieval, comparison, and reviewer-facing use of ICH M11-aligned and historical protocols

Translate approved ICH M11, Clinical Data Interchange Standards Consortium (CDISC), Unified Study Definition Model (USDM), ontology, metadata, and terminology models into practical schemas, mappings, JavaScript Object Notation (JSON) structures, and data-processing components under guidance from senior standards and architecture SMEs

Develop and support ETL/ELT pipelines, Python-based data transformations, and data preparation workflows for protocol content and structured AI outputs while preserving source provenance, metadata, validation status, and reviewer feedback

Support development and testing of large language model (LLM) workflows for protocol information extraction, summarization, classification, normalization, and comparison, including prompt configuration and structured-output generation

Assist with retrieval-augmented generation (RAG), embeddings, semantic search, vector indexing, evidence retrieval, and context assembly used to ground LLM responses in protocol content and approved reference sources

Support integration of LLM and data workflows with FDA-furnished Elsa/HALO capabilities and approved data services, including PostgreSQL and/or HALO/Databricks, for evidence caching, analytics, retrieval, and dashboard-ready outputs

Develop SQL and Python data-access logic and assist with APIs, JSON/XML processing, data validation, and automation supporting protocol extraction, historical retrieval, comparison, and source traceability

Execute data- and AI-focused testing, including checks for completeness, mapping consistency, retrieval quality, unsupported or hallucinated content, ambiguity, reproducibility, prompt behavior, and regression across approved configurations

Document data models, prompts, mappings, test results, interfaces, transformations, implementation decisions, and technical limitations to support client review, knowledge transfer, and iterative Proof of Concept (POC) refinement

Works closely with senior data engineers, clinical protocol SMEs, data standards specialists, architects, and AI/prompt engineers to translate approved requirements into working POC components

Participates in prompt and LLM testing, technical working sessions, POC demonstrations, validation activities, and iterative refinement based on client reviewer feedback

Supports implementation within provided platforms and approved data environments; the base effort is a proof of concept and does not require independent ownership of a production platform

Escalates ambiguous clinical, semantic, or AI-generated results to designated SMEs rather than making independent scientific or regulatory interpretations

Contributes practical data and AI engineering skills while developing deeper expertise in clinical protocols, USDM/M11 standards, semantic models, and regulated AI validation

Preferred

Working knowledge of SQL and relational databases such as PostgreSQL, Oracle, or SQL Server; exposure to data modeling, ETL/ELT, metadata, or data-quality practices

Experience working with JSON/XML, REST APIs, Git/version control, and cloud or data platforms is beneficial; exposure to AWS, Databricks, or similar platforms

Familiarity with RAG, embeddings, vector search/vector databases, semantic retrieval, or grounding techniques; experience can come from professional, academic, or personal projects

Databricks Fundamentals or Generative AI Fundamentals or AI Agent Fundamentals certification

SQL

PostgreSQL

Oracle

SQL Server

ETL/ELT

Metadata management

Data quality

REST APIs

Git

Version control

Cloud platforms

Data platforms

AWS

Databricks

Retrieval-augmented generation (RAG)

Embeddings

Vector search

Vector databases

Semantic retrieval

Grounding techniques

Databricks Fundamentals certification

Generative AI Fundamentals certification

AI Agent Fundamentals certification

Benefits

Hybrid – Columbia, MD

Company

IT and emerging technology solutions for government agencies

Columbia, Maryland, USA

https://www.apvit.com/

Founded in 2006

51-200 employees

Qualifications

Bachelor's degree in computer science, data science, computer engineering or related field

3-5 years of experience in data engineering, software development, data science, AI/ML, analytics, or a related technical role

Hands-on experience with Python for data processing, automation, API integration, or AI/ML workflows

Data modeling and working experience with relational databases, XML and JSON structure

Practical exposure to generative AI and large language models, including prompt development, LLM APIs, structured outputs, evaluation, or application development using modern AI frameworks

Must be able to obtain and maintain a High-Risk Public Trust or equivalent federal client access

Must be authorized to work in the United States without employer sponsorship now or in the future

Working knowledge of SQL and relational databases such as PostgreSQL, Oracle, or SQL Server; exposure to data modeling, ETL/ELT, metadata, or data-quality practices

Experience working with JSON/XML, REST APIs, Git/version control, and cloud or data platforms is beneficial; exposure to AWS, Databricks, or similar platforms

Familiarity with RAG, embeddings, vector search/vector databases, semantic retrieval, or grounding techniques; experience can come from professional, academic, or personal projects

Databricks Fundamentals or Generative AI Fundamentals or AI Agent Fundamentals certification

3-5 years of experience in data engineering, software development, data science, AI/ML, analytics, or a related technical role.

Required skills

Python, Data Modeling, Relational Databases, XML, JSON, Generative AI and Large Language Models, Prompt Development, SQL, ETL/ELT, REST APIs, Retrieval-Augmented Generation (RAG), High-Risk Public Trust Clearance

Experience

3-5 years