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About This Role
Description:
Founded in 2007, Initiate Government Solutions (IGS) is a Woman\-Owned Small Business and a fully remote IT services provider supporting federal partners nationwide. We deliver innovative Enterprise IT and Health Services solutions with a strong focus on data analytics, health informatics, cloud migration, AI, and the modernization of federal information systems.
Our vision is to be a health IT trendsetter, continuing to solve the nation’s most challenging healthcare IT issues by conceiving, designing, and building solid, creative, and innovative open\-source solutions.
Our mission is to innovate, design, and deliver tailored solutions that balance technical advancement with cost\-awareness while providing exceptional service.
IGS is currently recruiting a remote AI/ML Engineer to support our work with a federal customer.
Assignment of Work and Travel:
This is a remote access assignment. The Candidate will work remotely daily and will remotely access customer systems and therein use approved customer provided communications systems. Travel is not required; however, the candidate may be required to attend onsite client meetings as requested.
The AI/ML Engineer will play a critical role in designing, deploying, scaling, and supporting production\-grade AI/ML solutions within a secure and governed Azure cloud environment. This position will help enable predictive analytics, Natural Language Processing (NLP), generative AI capabilities, and advanced model development supporting improved healthcare outcomes and operational efficiencies for Veterans, their families, and caregivers.
Responsibilities and Duties (Included but not limited to):
- Design, implement, and maintain scalable AI/ML infrastructure using Azure Machine Learning, Databricks, Azure AI Services, and related cloud technologies.
- Develop and deploy predictive models, NLP solutions, and generative AI capabilities supporting enterprise business and healthcare use cases.
- Build reusable AI/ML frameworks, templates, and accelerators for model development, training, validation, deployment, and monitoring.
- Support AI/ML onboarding and adoption across customer workgroups utilizing shared analytics and AI capabilities.
- Integrate AI/ML workloads within secure DevSecOps environments using Infrastructure as Code (IaC) and automated security controls.
- Develop monitoring capabilities for model performance, bias detection, drift monitoring, and operational health management.
- Ensure AI/ML solutions align with zero\-trust architecture and security requirements.
- Design and deploy generative AI use cases utilizing Large Language Models (LLMs), prompt orchestration, guardrails, content filtering, and human\-in\-the\-loop validation.
- Implement NLP and text analytics solutions for structured and unstructured data sources, including healthcare and operational datasets.
- Collaborate with data engineers and analytics teams to operationalize AI\-generated insights and integrate AI outputs into business processes.
- Adhere to customer’s Trustworthy AI principles, privacy requirements, cybersecurity policies, and Risk Management Framework (RMF) controls.
- Support model governance, documentation, audit readiness, and AI risk assessment activities.
- Ensure compliance with federal regulations, accessibility standards, and enterprise data governance policies.
- Participate in Agile ceremonies including sprint planning, daily standups, retrospectives, backlog grooming, and release planning.
- Other duties as assigned
Requirements:
- 5\+ years of experience designing, developing, and deploying AI/ML solutions in cloud environments.
- Hands\-on experience with: Azure Machine Learning, Azure AI Services, Databricks, Python, ML frameworks such as TensorFlow, PyTorch, or Scikit\-Learn, Azure DevOps and CI/CD pipelines
- Experience developing and deploying NLP, machine learning, and predictive analytics solutions.
- Strong understanding of data engineering, cloud architecture, and distributed data processing.
- Experience with Git\-based source control and DevSecOps methodologies.
- Ability to obtain and maintain a Public Trust
- Ability to work in the United States without sponsorship
Preferred Qualifications and Core Competencies:
- Direct experience deploying AI/ML or data science solutions within a federal government agency.
- Experience supporting Department of Veterans Affairs (VA), Veterans Health Administration (VHA), Centers for Medicare \& Medicaid Services (CMS), or other federal healthcare environments.
- Experience with Generative AI, LLMs, Retrieval\-Augmented Generation (RAG), prompt engineering, and AI governance frameworks.
- Experience with Lakehouse architectures, Delta Lake, and enterprise data platforms.
- Familiarity with Zero Trust Architecture (ZTA), RMF, ATO processes, and federal cybersecurity requirements.
- Knowledge of healthcare datasets, clinical analytics, or healthcare data privacy requirements
- Active Public Trust
Successful IGS employees embody the following Core Values:
- Integrity, Honesty, and Ethics: We conduct our business with the highest level of ethics. Doing things like being accountable for mistakes, accepting helpful criticism, and following through on commitments to ourselves, each other, and our customers.
- Empathy, Emotional Intelligence: How we interact with others including peers, colleagues, stakeholders, and customers’ matters. We take collective responsibility to create an environment where colleagues and customers feel valued, included, and respected. We work within a diverse, integrated, and collaborative team to drive towards accomplishing the larger mission. We conscientiously and meticulously learn about our customers’ and end\-users’ business drivers and challenges to ensure solutions meet not only technical needs but also support their mission.
- Strong Work Ethic (Reliability, Dedication, Productivity): We are driven by a strong, self\-motivated, and results\-driven work ethic. We are reliable, accountable, proactive, and tenacious and will do what it takes to get the job done.
- Life\-Long Learner (Curious, Perspective, Goal Oriented): We challenge ourselves to continually learn and improve ourselves. We strive to be an expert in our field, continuously honing our craft, and finding solutions where others see problems.
Compensation: There are a host of factors that can influence final salary, including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications.
Benefits: Initiate Government Solutions offers competitive compensation and a robust benefits package, including comprehensive medical, dental, and vision care, matching 401K and profit sharing, paid time off, training time for personal development, flexible spending accounts, employer\-paid life insurance, employer\-paid short and long term disability coverage, an education assistance program with potential merit increases for obtaining a work\-related certification, employee recognition, and referral programs, spot bonuses, and other benefits that help provide financial protection for the employee and their family.
Initiate Government Solutions participates in the Electronic Employment Verification Program.
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 Initiate Government Solutions, 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.
Initiate Government Solutions AI Hiring
Initiate Government Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, 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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