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About This Role
DNI is seeking an AI Developer/Engineer to support an Artificial Intelligent Support Services program. This is a fully\-remote opportunity.
The AI Developer/Engineer designs, develops, integrates, and supports production\-ready artificial intelligence and machine learning solutions. This role works closely with software engineers, data scientists, product owners, and business stakeholders to translate mission and business needs into reliable, scalable, and maintainable AI\-enabled applications.
Key Responsibilities* Design, develop, test, and maintain machine learning, generative AI, and data\-driven applications.
- Translate business and user needs into technical requirements, solution designs, and implementation plans.
- Develop and integrate AI capabilities such as natural language processing, retrieval\-augmented generation, chatbots, document processing, classification, forecasting, anomaly detection, and recommendation services.
- Prepare, clean, transform, and analyze structured and unstructured data for use in AI and machine learning solutions.
- Select appropriate algorithms, models, frameworks, and evaluation methods based on solution requirements.
- Tune models and prompts to improve accuracy, relevance, reliability, latency, and cost efficiency.
- Collaborate with data scientists and software engineers to move prototypes and models into production environments.
- Build application components, APIs, services, and integrations that expose AI functionality securely and effectively.
- Implement automated testing, version control, configuration management, and repeatable deployment practices.
- Support infrastructure\-as\-code and cloud deployment activities using tools such as Terraform or comparable technologies.
- Contribute to CI/CD pipelines for application, data, and model delivery.
- Implement logging, monitoring, observability, and operational checks for AI\-enabled systems.
- Identify and address issues involving data quality, model drift, bias, hallucinations, performance, security, and maintainability.
- Document model designs, data sources, experiments, evaluation results, assumptions, limitations, operating procedures, and known risks.
- Participate in Agile ceremonies, sprint planning, demonstrations, retrospectives, peer reviews, and technical working sessions.
- Communicate technical findings and recommendations to both technical and non\-technical stakeholders.
- Follow applicable security, privacy, accessibility, compliance, and data\-governance requirements.
Requirements
Required Qualifications* Bachelor’s degree in computer science, software engineering, artificial intelligence, machine learning, data science, mathematics, or a related field; equivalent professional experience may be considered.
- Five or more years of experience in software development, machine learning engineering, data engineering, or a related discipline.
- Proficiency in Python and experience with at least one additional programming language such as JavaScript, TypeScript, Java, or R.
- Hands\-on experience with machine learning concepts, model development, evaluation, and performance improvement.
- Experience with one or more AI or deep\-learning frameworks such as PyTorch, TensorFlow, scikit\-learn, Hugging Face, or comparable tools.
- Experience developing APIs, services, or production applications that consume or provide AI functionality.
- Experience with Git\-based development, automated testing, and CI/CD practices.
- Working knowledge of cloud platforms and cloud\-native development patterns.
- Experience with data preparation, feature engineering, structured and unstructured data, and data\-quality practices.
- Strong analytical, troubleshooting, communication, and documentation skills.
- Must be a US citizen and pass a governmental\-level background check
Preferred Qualifications* Experience with generative AI, large language models, prompt engineering, embeddings, vector search, or retrieval\-augmented generation.
- Experience deploying AI solutions in regulated, public\-sector, healthcare, financial\-services, or other mission\-critical environments.
- Experience with Azure, AWS, or Google Cloud AI and machine learning services.
- Experience with Docker, Kubernetes, serverless services, or event\-driven architectures.
- Experience with model monitoring, explainability, responsible AI, AI security, or ML operations.
- Experience with relational databases, SQL, REST APIs, and modern web application development.
- Experience designing accessible user experiences and validating Section 508 or WCAG conformance.
- Experience working in Agile or Scrum teams.
Other Duties:
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.
Benefits Benefits Include:
- Covers 100% of employee benefit premiums, including Medical (PPO or HDHP Option), Vision, Dental
- Matching 401K
- Short\- and Long\-Term Disability
- Paid Federal Holidays Off
- Professional Development/Education Reimbursement
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Delaware Nation Industries, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Delaware Nation Industries AI Hiring
Delaware Nation Industries has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in OR, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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