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About This Role
### About Ascendion
Ascendion is an AI\-native software engineering disruptor helping businesses innovate faster, smarter, and with greater impact. We partner with enterprise clients across North America, the UK, Europe, and APAC to solve complex challenges in data, experience design, software product engineering, and workforce transformation. Powered by expert engineers, thousands of AI agents, and our Engineering to the Power of AI (EngineeringAI) method, we deliver measurable outcomes that build trust, unlock value, and accelerate growth.
Learn more at https://ascendion.com/ .
Engineering to the Power of AI™, AAVA™, Engineering AI , Engineering to Elevate Life™, Enterprise Platforms AI , Data \& Insights AI , Experience AI , GCC AI , Operations AI , Platform Engineering AI , Product AI , and Quality Engineering AI are trademarks or service marks of Ascendion ® . AAVA™ is pending registration. Unauthorized use is strictly prohibited.
### Ascendion \| Engineering to elevate life
We have a culture built on opportunity, inclusion, and a spirit of partnership. Come, change the world with us:
- Build the coolest tech for the world’s leading brands
- Solve complex problems – and learn new skills
- Experience the power of transforming digital engineering for Fortune 500 clients
- Master your craft with leading training programs and hands\-on experience
Experience a community of change makers!
Join a culture of high\-performing innovators with endless ideas and a passion for tech. Our culture is the fabric of our company, and it is what makes us unique and diverse. The way we share ideas, learning, experiences, successes, and joy allows everyone to be their best at Ascendion.
About the Role
------------------
We are seeking a highly motivated and hands\-on Healthcare AI Solution Engineer / Mid\-FDE with the mindset of a Forward Deployed Engineer (FDE).
Job Title
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AI Engineer
Key Responsibilities
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- Partner directly with healthcare business stakeholders to understand workflows, challenges, and strategic objectives across payer operations.
- Translate business needs into scalable, secure, cloud\-native technology solutions.
- Design enterprise solution architecture and build end\-to\-end applications across frontend, backend, APIs, integrations, data platforms, and AI capabilities.
- Develop production\-grade software using .NET, Python, modern frameworks, microservices, APIs, and engineering best practices.
- Drive cloud engineering, DevOps, CI/CD, Infrastructure as Code, automation, monitoring, and operational excellence.
- Leverage AI/LLMs, intelligent agents, and automation to improve healthcare operations, engineering productivity, and business outcomes.
- Identify modernization opportunities and proactively recommend innovative solutions.
- Own the complete solution lifecycle—from discovery, architecture, design, development, testing, deployment, and production support.
- Mentor engineering teams and establish engineering excellence through architecture standards, coding practices, and innovation.
Minimum Qualifications
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Required Healthcare Domain Experience:
- Strong expertise in one or more healthcare payer/provider domains.
Desired Qualifications
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- Experience with healthcare interoperability standards such as HL7/FHIR, APIs, and healthcare data exchange patterns is preferred.
Location
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Dallas, 75201
### Salary and Other Compensation:
The annual \[salary/hourly rate] for this position is between 130 \- 135K annually. Factors which may affect pay within this range may include geography/market, skills, education, experience, and other qualifications of the successful candidate.
### Benefits: The company offers the following benefits for this position, subject to applicable eligibility requirements: \[medical insurance] \[dental insurance] \[vision insurance] \[401(k) retirement plan] \[life insurance] \[long\-term disability insurance] \[short\-term disability insurance]. Change the world? Let us know.
Tell us about your experiences, education, and ambitions. Bring your knowledge, unique viewpoint, and creativity to the table. Let’s talk!
#### Preferred Skills
.NET
- Artificial Intellige
- Devops
- provider
- RAG
- HEALTHCARE
- SOLUTION ENGINEER
- Forward Deployed Engineer
#### Job details
###### Job ID
332702
###### Job Requirements
AI Engineer
###### Location
Dallas, Texas, US
###### Recruiter
Shrilekha
#### About Ascendion
###### Ascendion is a full\-service digital engineering solutions company. We make and manage software platforms and products that power growth and deliver captivating experiences to consumers and employees.
Our engineering, cloud, data, experience design, and talent solution capabilities accelerate transformation and impact for enterprise clients. Headquartered in New Jersey, our workforce of 6,000\+ Ascenders delivers solutions from around the globe. Ascendion is built differently to engineer the next.
Salary Context
This $130K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ascendion, 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. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $130K to $135K.
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.
Ascendion AI Hiring
Ascendion has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $135K - $135K.
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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