Interested in this AI/ML Engineer role at Qbitech?
Apply Now →Skills & Technologies
About This Role
About the Role
We're looking for an AI Engineer to join our team supporting the BioSurveillance Integration Platform (BIP), a mission-critical program delivering data-driven insight for biosurveillance, force health protection, and operational decision-making.
You'll design, develop, integrate, and operationalize AI/ML capabilities that support mission analytics, automated workflows, decision support, and intelligent applications. You'll work alongside data engineers, data scientists, platform engineers, architects, and customer stakeholders to transform mission requirements and trusted data into secure, production-ready AI solutions.
This role follows a phased work arrangement: fully remote during Phase 1 (stand-up and early build), transitioning to hybrid during Phase 2 to support integration, testing, and delivery milestones.
What You'll Do
- Design, develop, test, and deploy AI/ML solutions supporting biosurveillance, mission analytics, decision support, and operational workflows
- Build AI-enabled applications and workflows using large language models (LLMs), machine learning models, and other AI technologies
- Develop and integrate AI agents, Retrieval-Augmented Generation (RAG) systems, and LLM-powered workflows
- Integrate AI capabilities with enterprise data platforms, APIs, mission applications, and existing data pipelines
- Develop prompt engineering, structured output, tool-calling, and agentic workflow capabilities for mission use cases
- Build evaluation frameworks to measure model accuracy, reliability, relevance, latency, and operational performance
- Implement safeguards, validation, monitoring, logging, and human-in-the-loop controls for production AI systems
- Optimize AI applications for performance, scalability, reliability, and cost
- Evaluate models, frameworks, and emerging AI technologies for suitability within mission and security requirements
- Collaborate closely with data engineers, data scientists, platform engineers, architects, and mission analysts to deliver production-ready AI capabilities
- Produce architecture documentation, model documentation, evaluation results, AI workflow diagrams, runbooks, and release documentation
What You'll Need
- Bachelor's degree, or equivalent relevant experience
- Demonstrated experience developing AI/ML applications or AI-enabled software in a production or applied environment
- Proficiency in Python and experience with common AI/ML libraries and frameworks
- Experience working with LLMs, APIs, prompt engineering, or machine learning models
- Understanding of software engineering principles, REST APIs, Git, testing, and production deployment practices
- Experience integrating AI capabilities with structured and unstructured data
- Active Secret clearance, with ability to maintain it throughout the period of performance
- U.S. Citizenship
Nice to Have
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field
- Experience building agentic AI systems, RAG pipelines, vector search, tool-calling, or multi-agent workflows
- Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar AI orchestration frameworks
- Experience with commercial and open-source LLMs, including OpenAI, Anthropic, Google, Meta Llama, or Mistral models
- Experience deploying and operating models using Azure AI, AWS Bedrock/SageMaker, Google Vertex AI, or similar platforms
- Experience with Palantir Foundry, Maven Smart System, or other federal-scale data and AI platforms
- Experience with vector databases and semantic search technologies
- Familiarity with MLOps/LLMOps practices including model evaluation, versioning, monitoring, observability, and deployment
- Experience implementing AI guardrails, model evaluation, responsible AI practices, and human-in-the-loop workflows
- Experience deploying AI capabilities in restricted, disconnected, on-premises, or air-gapped environments
- Prior experience on federal/DoD programs operating under the Risk Management Framework (RMF)
- Familiarity with healthcare or biosurveillance data such as HL7 v2, HL7 FHIR, or DoD medical reporting formats
- AI/ML or cloud certification from AWS, Microsoft Azure, Google Cloud, NVIDIA, or equivalent
Tools & Platforms
AI/ML: Python, PyTorch, TensorFlow, Hugging Face, OpenAI APIs, Anthropic APIs, LangChain, LangGraph, LlamaIndex
Data & AI Platforms: Palantir Foundry, Maven Smart System, Apache Spark, Databricks
Cloud & MLOps: AWS Bedrock/SageMaker, Azure AI, Google Vertex AI, Docker, Kubernetes
Development & Collaboration: Git, REST APIs, Jira, Confluence, Microsoft Office 365
Benefits
- 401(k)
- 401(k) matching
- Health insurance
- Life insurance
- Referral program
Pay: $50,000.00 - $70,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Health insurance
- Life insurance
- Referral program
Work Location: Hybrid remote in Omaha, NE 68127
Salary Context
This $50K-$70K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Qbitech, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($60K) sits 72% below the category median. Disclosed range: $50K to $70K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Qbitech AI Hiring
Qbitech has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US. Compensation range: $70K - $70K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.