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About This Role
Overview:
LMI is seeking a Contact Center Training Specialist to support Product Acceptance \& Support (PAS) contact center operations for the United States Postal Service (USPS). This role will support training development, delivery, and continuous improvement across PAS\-supported contact centers and programs, including SHIPT, APV, PostalOne, FAST, Informed Delivery, BME, and Mailpiece Design.
The Contact Center Training Specialist will help assess training needs, develop and maintain training materials, deliver onboarding and operational training, and support advanced subject matter expert (SME) training for Tier 1, Tier 2, and Tier 3 support environments. This position will also help ensure training content aligns with the PAS common contact center technology stack, including Genesys Cloud, Salesforce, ServiceNow, and AIVA\-enabled workflows where applicable.
This position is ideal for a proactive, detail\-oriented professional who enjoys working in a fast\-paced environment, collaborating with multiple stakeholders, and improving workforce readiness and operational performance. Remote work is acceptable, with flexibility to work onsite based on client request.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial\-grade platforms and mission\-ready AI to federal agencies at commercial speed.
Leveraging our mission\-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
Responsibilities:
- Assess training needs and identify gaps related to new\-hire onboarding, day\-to\-day contact center operations, and advanced SME support.
- Develop, update, and maintain training curricula, instructional materials, job aids, reference guides, and e\-learning content for PAS\-supported systems and processes.
- Deliver training to agents and SMEs through in\-person, virtual, and blended learning formats.
- Support onboarding and recurrent training for Tier 1, Tier 2, and Tier 3 support personnel across PAS\-supported contact center operations.
- Ensure training content reflects operational processes and aligns with the PAS technology stack, including Genesys Cloud, Salesforce, ServiceNow, and AIVA\-enabled workflows where applicable.
- Coordinate with PAS leadership, contact center managers, and SMEs to validate training priorities, content accuracy, and readiness needs.
- Facilitate knowledge transfer and support standardization of training approaches across multiple PAS\-supported programs.
- Measure training effectiveness through feedback, completion tracking, knowledge checks, and other performance indicators.
- Recommend and implement continuous improvements to training content, delivery methods, and support materials.
- Prepare training status updates, completion summaries, and other reports as requested by PAS leadership.
Qualifications:
Required
- 4\+ years of experience supporting contact center training, workforce development, or operational training programs.
- 2\+ years of experience developing and delivering training in a multi\-team, customer support, or enterprise operations environment.
- Experience creating and maintaining training materials, job aids, and instructional content.
- Experience delivering training in virtual and/or in\-person environments.
- Strong written, verbal, facilitation, and stakeholder coordination skills.
- Ability to manage multiple priorities and work effectively in a dynamic operational environment.
- Bachelor’s degree required.
- Self\-motivated, reliable, and dependable with strong interpersonal and communication skills.
Preferred
- Experience supporting federal contact center or customer support operations.
- Experience supporting USPS programs or modernization initiatives.
- Familiarity with Tier 1, Tier 2, and Tier 3 support models.
- Experience with cloud\-based contact center and case management platforms such as Genesys Cloud, Salesforce, and ServiceNow.
- Experience developing training for AI\-enabled or digitally supported service operations.
- Knowledge of large\-scale government transformation or shared services environments.
Additional Information
- Ability to obtain security clearance: Public Trust
Target Salary Range: $63,000 \- $107,000 *Disclaimer:*
The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.
Salary Context
This $63K-$107K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At LMI, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($85K) sits 49% below the category median. Disclosed range: $63K to $107K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
LMI AI Hiring
LMI has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Washington, DC, US. Compensation range: $90K - $189K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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