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Title: AI Service Experience Analyst
Location: Remote in USA
Position Summary
The AI Service Experience Designer will shape, refine, and continuously improve AI-enabled service experiences that are intuitive, accurate, compliant, and aligned to business outcomes. This role owns the business design of AI journeys, prompt frameworks, agent instructions, knowledge readiness, testing methods, and performance feedback loops—partnering closely with AI Engineers, product, technology, operations, compliance, governance, and vendor teams to bring scalable AI solutions to life.
Responsibilities
- Own the business setup and ongoing refinement of AI-enabled service experiences, including experience objectives, user journeys, conversational flows, agent instructions, escalation paths, and desired outcomes.
- Design, test, and optimize prompts, agent instructions, and interaction patterns for generative AI tools, AI agents, knowledge assistants, and other AI-enabled service solutions.
- Assess, structure, modify, and maintain knowledge base content, procedures, job aids, scripts, and source materials to improve AI readiness, retrieval quality, response accuracy, and consistency.
- Provide business oversight of AI agents by monitoring performance, reviewing outputs, identifying gaps or risks, validating behavior against business expectations, and recommending corrective actions.
- Define business requirements, test scenarios, acceptance criteria, quality standards, and success measures for AI-enabled service experiences and agent performance.
- Conduct structured testing and quality reviews of prompts, knowledge retrieval, agent responses, conversational experiences, and end-to-end service flows using experimentation, user feedback, and performance data.
- Analyze AI outputs for accuracy, relevance, clarity, consistency, usability, bias, risk, compliance considerations, and alignment with business intent; iterate and improve as needed.
- Collaborate with technology, product, data, compliance, governance, operations, and vendor partners to ensure AI-enabled experiences are technically feasible, secure, scalable, governed, and aligned with approved AI guardrails.
- Partner with AI Engineers to translate business-owned experience designs, prompt logic, agent behavior expectations, knowledge requirements, test findings, and performance gaps into engineering-ready requirements, build considerations, troubleshooting needs, and enhancement recommendations.
- Create and maintain reusable prompt frameworks, agent playbooks, knowledge readiness standards, testing methods, documentation, and business guidance to support consistent AI adoption and continuous improvement.
- Measure and communicate AI experience effectiveness, agent performance, adoption, efficiency gains, service quality improvements, knowledge quality improvements, and contribution to business value or ROI targets.
- Apply healthcare service journey and experience design principles to identify pain points, map current and future-state experiences, and shape AI-enabled solutions that support operational needs and improve employee, member, provider, or stakeholder experiences.
- Stay current on emerging AI capabilities, prompt and agent design methods, knowledge management practices, model behavior, evaluation techniques, healthcare payer trends, and industry best practices.
- Perform any other job-related duties as requested.
Qualifications
- Experience designing or improving AI-enabled service experiences, conversational flows, user journeys, knowledge assistants, AI agents, or generative AI interactions.
- Strong understanding of prompt design, agent instructions, interaction patterns, knowledge retrieval, and AI response evaluation.
- Experience working with knowledge base content, procedures, scripts, job aids, or source materials to improve accuracy, consistency, usability, and AI readiness.
- Ability to define business requirements, acceptance criteria, quality standards, test scenarios, and success measures for digital or AI-enabled solutions.
- Strong analytical skills with the ability to review AI outputs for accuracy, relevance, clarity, consistency, usability, bias, risk, and business alignment.
- Experience collaborating with cross-functional teams, including technology, product, data, operations, compliance, governance, vendors, and engineering partners.
- Ability to translate business expectations, experience designs, knowledge needs, prompt logic, and test findings into clear requirements and actionable recommendations.
- Strong documentation skills, including the ability to create reusable playbooks, frameworks, standards, testing methods, and business guidance.
- Comfortable working in an evolving AI environment with a continuous improvement mindset and a focus on measurable business value.
- Healthcare, healthcare payer, member/provider service, or service journey design experience is a plus.
- Familiarity with AI governance, approved AI guardrails, compliance considerations, and risk-aware design practices is a plus.
The base salary for this position is $92,250 to $120,000 plus incentives that align with individual and company performance. Actual salaries will vary based on work location, qualifications, skills, education, experience, and competencies. Benefits available to eligible employees in this role include medical, dental, and vision insurance, comprehensive employee assistance program, 401(k) retirement plan, paid time off and holidays and paid learning days.
The deadline to apply for this position is: 10/15/2026. This position is for an existing, immediate vacancy. We are currently seeking to fill this role with an individual who can start as soon as possible.
As part of the hiring process, candidates may be required to undergo background screening and identity verification, where permitted by applicable law and consistent with the requirements of the role. Certain verification processes used by the Company or its service providers may involve technologies that rely on biometric identifiers or biometric information, where permitted by law. If biometric identifiers or biometric information are collected, used, or stored, the Company will provide the legally required disclosures and obtain any required written consent prior to such collection, and will handle such information in accordance with applicable biometric privacy laws and Company policies.
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Physical and Mental Requirements:
The employee is regularly required to operate a computer, keyboard, telephone/headset, and/or other office equipment as essential functions of this position. Work is generally sedentary in nature.
Equal Employment Opportunity:
Concentrix is an equal opportunity and affirmative action (EEO-AA) employer. We promote equal opportunity to all qualified individuals and do not discriminate in any phase of the employment process based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy or related condition, disability, status as a protected veteran, or any other basis protected by law.
For more information regarding your EEO rights as an applicant, please visit the following websites:
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Accommodation:
Concentrix welcomes and encourages applications from candidates with disabilities and is committed to providing an inclusive recruitment process. If you require reasonable accommodation to participate in any stage of the application or interview process, please let us know. Requests may be made by contacting [email protected]. All information will be treated confidentially and used solely to facilitate your participation in the recruitment process.
Artificial Intelligence:
As part of our recruitment process, we may use artificial intelligence (AI) tools to assist in the screening and/or assessment of job applicants. These tools could be used to evaluate resumes, applications, and other materials submitted to help us identify the best candidates for the role.
Work Authorization:
In accordance with federal law, only applicants who are legally authorized to work in the United States will be considered for this position. Must reside in the United States or have a valid U.S. address for residence.
For further information on available work states and Equal Employment Opportunity as an applicant, please click HERE
Salary Context
This $92K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Concentrix, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($106K) sits 50% below the category median. Disclosed range: $92K to $120K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Concentrix AI Hiring
Concentrix has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Columbus, OH, US, National Capital Region, Philippines, Bellevue, WA, US. Compensation range: $120K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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