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About This Role
Job Description:
The Data \& AI Solution Engineer operates within the Data Intelligence team and is responsible for designing, building, and operating enterprise\-grade data, integration, and AI\-enabling solutions across Flexential.
This role extends beyond traditional data engineering to include system\-to\-system integration using Boomi, orchestration of business\-critical workflows, and active participation in the design and delivery of AI\-enabled solutions. The role partners closely with enterprise architecture, application teams, and business stakeholders to ensure scalable, secure, and well\-governed data and AI foundations.
The position adheres to best practices including Agile delivery, solution design documentation, code quality standards, and operational transparency.Key Responsibilities and Essential Job Functions
Data Acquisition, Integration, and Orchestration
- Design and implement data and application integrations using Boomi, APIs, and event\-driven patterns
- Build and manage secure, scalable integrations across enterprise platforms including ERP, CRM, operational systems, and data platforms
- Monitor, troubleshoot, and optimize integration workflows for reliability and performance
Data Engineering and Platform Enablement
- Extract, transform, and model structured and semi\-structured data for analytics and downstream consumption
- Implement and manage data storage solutions including data lakes, data warehouses, and curated data marts
- Apply business rules and data quality standards to ensure trusted, fit\-for\-purpose datasets
AI Solution Design and Enablement
- Partner with architects, data scientists, and business teams to design solution patterns that support AI and automation use cases
- Enable AI solutions by engineering clean, well\-integrated, and governed data inputs
- Contribute to solution design for AI\-enabled capabilities including automation, decision support, and analytics
Pipeline and Workflow Development
- Build and manage automated data and integration pipelines delivering consistent outcomes
- Develop reusable integration and data components across the Agile delivery team
- Participate in design reviews and code reviews to ensure maintainability and architectural alignment
Collaboration and Delivery
- Communicate effectively with technical and business stakeholders
- Manage personal delivery commitments and dependencies
- Contribute to technical documentation, operational runbooks, and knowledge sharing
Required Qualifications
- Experience working in an Agile delivery environment
- 3\+ years of experience in data engineering, solution engineering, or integration engineering roles
- Strong SQL development experience in a Microsoft SQL Server environment
- Hands\-on experience with integration platforms such as Pentaho, Boomi or equivalent iPaaS tools
- Experience working with APIs, data exchange patterns, and workflow orchestration
- Proficiency with tools such as GitHub, Python, and enterprise data tooling
- Strong understanding of relational database principles, data modeling, and performance optimization
Preferred Qualifications (Not Required)
- Experience supporting AI or machine learning use cases
- Familiarity with enterprise SaaS platforms and operational systems
- Knowledge of data center operations or billing platforms
Base Pay Range: Annualized salary range offered for this position is estimated to be$100,000 \- $110,000\. However, the actual pay range depends on each candidate’s experience, location, and qualifications.
Variable Pay: Discretionary annual bonus, based on personal and company performance.
*Not meeting every single requirement? No problem! We are looking for candidates who possess unique skills that set them apart from the rest. If you're enthusiastic about this role and believe you have the skills and abilities that would make you successful, don't hesitate to apply today!*
Benefits of working at Flexential:
- Medical, Telehealth, Dental and Vision
- 401(k)
- Health Savings Accounts (HSA) and Flexible Spending Accounts (FSA)
- Life and AD\&D
- Short Term and Long\-Term disability
- Flex Paid Time Off (PTO)
- Leave of Absence
- Employee Assistance Program
- Wellness Program
- Rewards and Recognition Program
Benefits are subject to change at the Company's discretion.
Flexential participates in the E\-Verify program. Please click here for more information.
EEOC Statement: Flexential is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Flexential, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Flexential AI Hiring
Flexential has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Centennial, CO, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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