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About This Role
Job Description
Join Aperia Solutions, a leader in SaaS solutions for the Payments and Compliance industries. Aperia is a Texas\-based fintech and managed consultancy firm that creates custom SaaS applications and other software\-based solutions for the payments, banking, and processing industry. Founded in 1999, Aperia offers business intelligence, risk management, compliance, and customer intelligence platforms. With offices in Dallas, Washington DC, and Vietnam, Aperia is a fast\-paced, global organization that strives to improve efficiency in compliance, risk, and customer service operations. Aperia's clients include banks, processors, payment facilitators, merchant service providers, independent sales organizations, and government entities. A career at Aperia promises a great challenge, culture, and opportunities to forge your own path.
We are seeking an experienced AI/LLM Software Engineer to join our growing development team and help design, build, and integrate intelligent solutions into modern enterprise applications.
The ideal candidate has hands\-on experience working with Generative AI, Large Language Models (LLMs), AI\-assisted software development, and AI\-powered applications. You will work closely with software engineers, architects, business stakeholders, and product teams to identify opportunities where AI can improve productivity, automation, data intelligence, and customer experiences.
This is a hands\-on engineering role. Candidates should have a solid foundation in software development and APIs, with some practical experience in C\#/.NET. Deep expertise in .NET, React, or Angular is not required. We are more interested in candidates who understand modern AI/LLM technologies and can apply them effectively within enterprise software environments.
Key Responsibilities:
- Design, develop, and integrate AI/LLM\-powered capabilities into enterprise applications.
- Evaluate and integrate LLM platforms, models, APIs, and AI services such as Azure OpenAI, OpenAI, or similar technologies.
- Develop solutions using techniques such as:
- Prompt engineering
- Retrieval\-Augmented Generation (RAG)
- Embeddings and semantic search
- Vector databases
- Function/tool calling
- Structured outputs
- AI agents and agentic workflows
- Context management
- Build and consume RESTful APIs to integrate AI capabilities with existing enterprise applications.
- Develop proof\-of\-concepts and production\-ready AI solutions while evaluating model quality, accuracy, performance, cost, and scalability.
- Leverage GitHub Copilot and other AI coding assistants to improve software development productivity while maintaining code quality and security.
- Develop appropriate approaches for AI evaluation, testing, monitoring, and validation, including identifying hallucinations and unreliable model responses.
- Work with business stakeholders to identify practical use cases for AI and translate business requirements into technical solutions.
- Integrate AI solutions with enterprise data sources, databases, APIs, and existing applications.
- Participate in architecture and technical design discussions related to AI\-enabled applications.
- Ensure AI solutions follow enterprise security, privacy, compliance, and DevSecOps practices.
- Research emerging AI/LLM technologies and recommend approaches that can provide business value.
- Troubleshoot complex technical issues involving applications, APIs, AI services, data, and infrastructure.
- Mentor team members and share knowledge related to AI/LLM technologies and best practices.
Required Skills and Experience
AI / LLM — Primary Focus
- 2\+ years of software engineering experience with hands\-on exposure to Generative AI and/or LLM technologies.
- Practical experience integrating LLMs or AI services into applications.
- Understanding of LLM concepts such as:
- Prompt engineering
- Tokens and context windows
- Embeddings
- Vector search
- RAG
- Fine\-tuning concepts
- Function/tool calling
- AI agents
- Model evaluation
- Experience working with one or more LLM/AI platforms such as Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, or similar.
- Experience developing AI\-powered applications or prototypes using APIs, SDKs, or AI frameworks.
- Experience using GitHub Copilot, ChatGPT, Claude, or other AI\-assisted development tools.
- Ability to understand, review, debug, and improve AI\-generated code rather than simply relying on AI\-generated output.
- Understanding responsible AI, including security, privacy, hallucination risks, data protection, and appropriate handling of sensitive information.
Software Engineering
- 4\+ years of professional software development experience.
- Some hands\-on experience with C\#/.NET or .NET Core/.NET 6\+. Deep .NET expertise is not required.
- Strong understanding of software engineering fundamentals, including:
- Object\-Oriented Programming
- SOLID principles
- Design patterns
- Algorithms and data structures
- Clean and maintainable code
- Experience developing and consuming RESTful APIs.
- Experience working with relational databases such as SQL Server, PostgreSQL, or similar.
- Experience with Git and modern source\-control workflows.
- Understanding of CI/CD and modern software development practices.
- Strong debugging, troubleshooting, and problem\-solving skills.
- Experience working in Agile/Scrum environments.
Cloud / DevOps / Security
- Experience working with cloud\-based applications, preferably Azure, AWS, or Google Cloud.
- Understanding cloud\-native application architecture and microservices.
- Familiarity with Docker and/or Kubernetes is a plus.
- Experience with CI/CD tools such as Azure DevOps, GitHub Actions, Jenkins, or Harness.
- Understanding of secure software development and DevSecOps practices.
- Familiarity with SAST/security tools such as Fortify or similar technologies is a plus.
- Experience with application monitoring and observability tools such as Splunk or Dynatrace is a plus.
Preferred Experience
- Experience building enterprise AI/LLM applications.
- Experience implementing RAG solutions using enterprise documents or databases.
- Experience with vector databases such as Pinecone, Azure AI Search, Weaviate, Milvus, pgvector, or similar.
- Experience with AI/LLM frameworks such as LangChain, Semantic Kernel, LlamaIndex, or similar.
- Experience with AI agents and tool/function calling.
- Experience developing AI evaluation and testing strategies.
- Experience integrating LLMs with enterprise APIs and business systems.
- Familiarity with Azure AI services and Azure OpenAI.
- Experience with financial services, banking, payments, or other high\-volume transactional systems.
- Experience with APIGEE or API management platforms.
- Experience with MongoDB, Cassandra, or other NoSQL technologies.
- Experience with React, Angular, TypeScript, or other modern frontend technologies is a plus, but not required.
Education or Certifications
- Bachelor's degree in computer science, Information Systems, or another related field.
Eligibility Requirements
- Must be willing to submit to a background investigation and drug test as part of the selection process.
Job Type
- Full time
Schedule
- Monday to Friday
Work Location
- Alpharetta, GA
- Omaha, NE
- Frisco, TX
Benefits
- Health insurance
- Health savings account
- Dental insurance
- Vision insurance
- 401(k) matching
- Life insurance
- Paid time off
- Parental leave
- Disability insurance
- Childcare assistance
- Education reimbursement
- Fitness membership
- Volunteer time off
*This job description is not intended to be all\-inclusive. An employee may also perform other reasonable related business duties as assigned by their immediate supervisor or management. Principals only.*
*Recruiters please don't contact this job poster. DO NOT contact us with unsolicited services or offers.*
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 Aperia, 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.
Aperia AI Hiring
Aperia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Frisco, TX, 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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