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About This Role
Status: Hybrid 3-4 Days onsite in Washington DC
Experience: 5-6 years
Requires: Public Trust Sensitive - US Citizen and Green Card
Benefit: Competitive Pay, Generous PTO and Holiday Schedule, Comprehensive Health Plan, 401(k), and Collaborative Team Atmosphere
Salary Range - 95k to 100K
Summary: Assurety is an industry leader in the postal and parcel ecosystem, with 21+ years of experience serving government and commercial sectors, including postal/parcel, marketing, finance, retail, and higher education. We leverage data and technology to solve complex problems, fostering a culture of collaboration, innovation, and ownership. Our solutions help clients save lives, cut costs, boost revenue, strengthen the U.S. economy, and enhance customer experiences.
This is a consulting role focused on analyzing complex business needs and translating them into actionable technical requirements, with a strong emphasis on AI-enabled analysis and modeling. We’re not looking for someone who just prompts a chatbot, we need professionals who can use AI tools, data modeling, and automation to accelerate requirements gathering and improve solution quality. This is a rare opportunity to join a highly stable, long-term program where your work will have a direct impact on one of the nation’s most important delivery networks.
Key Responsibilities
- Partner with business and technical stakeholders to elicit, analyze, and document complex requirements for enterprise USPS systems, including APIs, price change workflows, compliance systems, and customer-facing applications.
- Translate stakeholder needs into clear, actionable deliverables including user stories, workflows, data models, and technical specifications.
- Use AI tools and modeling techniques to enhance requirements development, automate repetitive tasks, and perform data-driven analysis to improve accuracy and efficiency.
- Leverage AI-driven techniques to prototype workflows, generate user stories, and validate requirements, enabling stakeholders to visualize solutions early and reduce rework.
- Work with data, APIs, and integration points; validate data flows and ensure solutions meet technical and business needs.
- Collaborate closely with developers, architects, testers, and product owners to ensure seamless translation of requirements into solutions.
- Produce high-quality documentation including requirements matrices, test plans, training materials, and technical guides.
- Facilitate meetings, workshops, and presentations to align diverse stakeholders and resolve competing priorities.
- Support Agile and Waterfall methodologies, adapting deliverables to project needs.
- Contribute to quality assurance and UAT efforts to confirm that deliverables meet business and technical goals.
Qualifications
Required
- U.S. Citizenship or Green Card; able to obtain Public Trust clearance.
- 5+ years of business, technical, and systems analysis experience, including extensive client-facing work.
- Bachelor’s degree in Computer Science, Business Administration, Engineering, Economics, MIS, or related field.
- Exceptional written and verbal communication skills, including the ability to prepare clear documentation and train stakeholders.
- Proven ability to independently create technical solutions for business problems, from discovery through implementation.
- Strong analytical skills and proficiency working with XML, JSON, SQL, and relational databases.
- Adept at facilitating discussions and balancing multiple priorities across stakeholder groups.
- Experience integrating modern tools into requirements analysis; exposure to AI/ML techniques (e.g., Python, TensorFlow, Azure AI, or similar frameworks).
- Familiarity with Jira, Confluence, and other requirements management and documentation tools.
- Proficiency with Microsoft Excel (pivot tables), PowerPoint, Visio/Lucid, and SQL.
Preferred
- Experience supporting USPS programs or large-scale mailing operations.
- Familiarity with (COTS) mailing software solutions, Salesforce, or enterprise API environments.
- Experience architecting solutions for various systems involving flowcharts, diagrams, ERDs, and technical user stories.
- Prior consulting or professional services experience.
What Success Looks Like in This Role
- You become a trusted partner for stakeholders, delivering clear, actionable requirements.
- You consistently meet or exceed deadlines with minimal oversight.
- You identify risks early and recommend solutions to avoid delivery issues.
- You leverage AI and advanced tools to improve efficiency and quality.
- Your work results in fewer requirement changes and improved project delivery for USPS initiatives.
Salary Context
This $95K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Assurety Consulting Inc, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $95K to $100K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Assurety Consulting Inc AI Hiring
Assurety Consulting Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $100K - $100K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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