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About This Role
POSITION TITLE: Data & AI Engineer
LOCATION: Hybrid – Columbia, MD - Must reside in DMV area
CLEARANCE: Must be able to obtain a High-Risk Public Trust
COMPANY OVERVIEW
At APV, we’re more than a technology company — we’re a mission-driven powerhouse transforming organizations through advanced technology and human ingenuity. Our expertise spans AI/ML, data architecture, low-code/no-code development, Agile DevSecOps, and cloud services, delivering scalable and meaningful solutions.
In our Emerging Technology Lab, innovation drives progress. Our teams create intelligent chatbots, AI-powered assistants, robotic process automation (RPA), essay graders, and data analytics platforms. If you’re passionate about solving complex challenges and shaping the future, APV is the place for you. Since 2007, we’ve partnered with federal and state agencies to deliver IT, training, and consulting solutions that achieve mission-critical outcomes. Built on accountability, integrity, and quality, we go beyond expectations. With 70+ prime contracts and a proven record of client success, APV continues to grow — and we’re looking for exceptional talent to grow with us.
At APV, we Always Provide Value
POSITION SUMMARY
Provides 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 works under senior technical and domain guidance to prepare protocol data, implement structured data components, configure and test LLM-based extraction workflows, and support traceable retrieval, comparison, and validation within federal approved environments.
DUTIES
- 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.
EDUCATION
- Bachelor’s degree in computer science, data science, computer engineering or related field
REQUIRED QUALIFICATIONS
- 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. This role supports sensitive federal programs and involves elevated access to systems and data.
- Position requires the current ability to obtain and maintain required access without interruption in alignment with long-term program needs.
- Must be authorized to work in the United States without employer sponsorship now or in the future.
PREFERRED QUALIFICATIONS
- 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 is strongly preferred; experience can come from professional, academic, or personal projects.
- Databricks Fundamentals or Generative AI Fundamentals or AI Agent Fundamentals certification
ABOUT APV
APV is an Equal Employment Opportunity employer. All qualified applicants are considered without regard to race, color, religion, sex, national origin, age, disability, genetic information, sexual orientation, gender identity, veteran status, or marital status.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At A P Ventures LLC, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
A P Ventures LLC AI Hiring
A P Ventures LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbia, MD, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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