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About This Role
Title: Agentic AI and DevSecOps Engineer
Location: Hybrid in DMV/Remote may be considered
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
We are seeking a highly skilled and hands\-on Agentic AI/DevSecOps Engineer to join our dynamic team. The ideal candidate will be collaborative, innovative, and capable of providing effective AI solutions for various internal and federal projects. This role requires a deep understanding of legacy applications, emerging technologies, AI technologies, strong problem\-solving skills, and the ability to work closely with cross\-functional teams.
Duties:
Agentic System Design \& Development
· Design and build Agentic AI systems using frameworks such as LangChain, Lang Graph, MCP, or similar
· Develop multi\-agent or task\-oriented architectures for autonomous workflows
· Integrate AI agents with enterprise APIs, data platforms, and external tools
· Define agent workflows, orchestration logic, and tool\-use strategies
· Evaluate when to use LLM\-based vs traditional approaches
AI Engineering \& Data Systems
· Build RAG pipelines using embeddings and vector databases
· Implement vector search and retrieval strategies for knowledge\-based agents
· Work with structured and unstructured data for AI\-driven applications
· Apply AI/ML fundamentals to improve agent reasoning and performance
Testing, Evaluation \& Optimization
· Implement unit and integration testing for AI\-enabled systems
· Optimize systems for accuracy, latency, cost, and reliability
· Validate and monitor AI outputs for quality and consistency
· Develop testing and evaluation pipelines for agentic workflows and LLM systems
Deployment, DevSecOps \& Infrastructure
· Implement secure DevSecOps pipelines for AI applications
· Deploy solutions in AWS cloud environments
· Build and manage infrastructure using IaC tools
· Work with containerized applications (Docker/Kubernetes)
· Ensure systems meet security and compliance requirements (especially federal environments)
General Responsibilities
· Deliver production\-grade, scalable AI agent solutions
· Support Agile development practices and iterative delivery
· Document architecture, workflows, and decisions
· Stay current with advancements in Agentic AI and LLM technologies
Education:
· Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field
Master's degree in a scientific discipline (Preferred)
Required Qualifications:
· 1–2 years of hands\-on experience implementing or deploying Agentic AI solutions
· 3\+ years of Agile software development and DevSecOps experience
· Experience building AI/ML\-enabled systems, including LLM\-based applications
· Experience implementing Infrastructure as Code (IaC)
· Strong programming skills in Python (primary); JavaScript (preferred)
· Working knowledge of:
o Linux environments
o SQL and relational databases
o Vector databases
Preferred Qualifications:
· AWS Certification
· Experience deploying AI/ML solutions in federal or regulated environments
· Experience with:
o RAG architectures and retrieval optimization
o Multi\-agent systems and orchestration workflows
o AI system evaluation and observability
About A P Ventures
A P Ventures is an Equal Employment Opportunity employer. All qualified applicants are considered without regard to race, national origin, gender, age, religion, disability, sexual orientation, 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
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, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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