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About This Role
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Position Description
The Microsoft AI Developer will focus on building innovative solutions using Azure AI, Power Platform, Microsoft 365, and modern software engineering practices. The ideal candidate is a hands-on engineer who can rapidly prototype, develop, and productize solution assets that improve delivery quality, reduce implementation costs, and provide Federal customers transformative value. Responsibilities include:
Design and develop reusable solutions built on Microsoft Power Platform, Azure, and Microsoft 365.
Build modern web applications using React, TypeScript, .NET, and Azure services.
Develop AI-powered business solutions utilizing Azure AI Foundry, Azure AI Search, Document Intelligence, and related services.
Develop custom Copilot experiences, Copilot Studio agents, conversational AI solutions, and retrieval-augmented generation (RAG) architectures.
Create reusable Power Platform assets including PCF controls, starter kits, ALM accelerators, deployment frameworks, and governance components.
Leverage GitHub Copilot, AI-assisted development tools, and modern software engineering practices to maximize development velocity.
Contribute to architecture standards, engineering best practices, reusable design patterns, and technical documentation.
Collaborate with delivery teams to operationalize accelerator assets and promote adoption across the organization.
The projected compensation range for this position is $100K - $175K annualized (USD). The final salary offered will generally fall within this range and is determined by various factors, including but not limited to the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as internal pay equity, location, contract-specific affordability and other organizational requirements.
Required Skills
5+ years of software development experience designing and implementing enterprise applications.
Strong hands-on experience with React, TypeScript, JavaScript, HTML, and modern front-end development frameworks.
Strong experience developing applications using C# and .NET.
Experience building and deploying applications within Microsoft Azure.
Hands-on experience with Azure Functions, App Services, Container Apps, Logic Apps, API Management, and other Azure PaaS services.
Experience developing solutions using Microsoft Power Platform, including Dataverse, Power Apps, Power Automate, Power Pages, and Code Apps.
Experience integrating applications with Microsoft Graph and Microsoft 365 services.
Experience utilizing Azure OpenAI, Azure AI Foundry, Azure AI Search, Document Intelligence, or related AI technologies.
Experience designing REST APIs and integrating enterprise systems.
Experience with GitHub, source control, CI/CD pipelines, and DevOps practices.
Familiarity with GitHub Copilot, Claude, Cursor, and AI-assisted software development practices.
Strong understanding of software architecture, security, scalability, and cloud-native application design.
Excellent written and verbal communication skills.
Ability to rapidly learn and evaluate emerging technologies.
Desirable Skills
Experience developing Copilot Studio agents and custom Copilot extensibility solutions.
Experience implementing RAG architectures, semantic search solutions, vector databases, and AI orchestration frameworks.
Experience developing Power Apps Component Framework (PCF) controls.
Experience with Microsoft Teams application development and Microsoft Graph extensibility.
Experience with Azure AI Foundry evaluation, prompt engineering, model selection, and AI governance.
Experience building reusable enterprise software products, frameworks, or commercial accelerators.
Experience with Next.js, Fluent UI, and modern React ecosystem tools.
Experience with Infrastructure as Code using Bicep, Terraform, or ARM templates.
Experience with Azure Kubernetes Service (AKS) and containerized application architectures.
Experience supporting federal government customers.
Microsoft certifications in Azure, AI, Power Platform, or Microsoft 365 technologies.
Job ID
Procentrix is an Equal Opportunity employer and does not discriminate on the basis of race, color, religion, gender, national origin, age, marital or veteran status, the presence of a non-job related medical condition or handicap, or any other legally protected status.
Salary Context
This $100K-$175K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Procentrix, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $100K to $175K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Procentrix AI Hiring
Procentrix has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
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