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Overview of Job Function:
The Sr. Conversational AI Consultant serves as a strategic advisor and lead consultant for customers leveraging AI\-powered customer experience and operational solutions. This role partners closely with customer stakeholders, executive leaders, and internal cross\-functional teams to drive successful solution adoption, optimize business processes, and deliver measurable business outcomes through strategic consulting, analytics, and technical expertise.
Serving as the consulting lead on customer engagements, the Sr. Conversational AI Consultant is responsible for guiding solution strategy, business transformation initiatives, and value realization efforts while partnering with Program Managers to ensure successful execution. Through trusted advisor relationships and industry expertise, this role helps organizations improve operational performance, enhance customer experiences, and maximize the value of their AI investments.
Principal Duties and Essential Responsibilities:
Customer Consulting \& Solution Optimization
- Serve as the lead consultant and primary strategic advisor for customers utilizing AI\-powered solutions, providing guidance throughout the customer lifecycle.
- Lead the consulting workstream for customer engagements, driving solution adoption, optimization, and business transformation initiatives.
- Partner with customer stakeholders to understand business objectives, operational challenges, and success criteria, recommending solutions that deliver measurable business outcomes.
- Establish trusted advisor relationships with business leaders, operational stakeholders, and executive sponsors.
- Facilitate discovery sessions, workshops, business reviews, and stakeholder discussions to align priorities, define success measures, and drive customer success.
- Develop strategic success plans in collaboration with Program Managers, focusing on measurable business outcomes, adoption goals, and value realization.
- Identify opportunities to increase solution utilization, improve operational effectiveness, and expand customer value.
AI Subject Matter Expertise
- Serve as a senior subject matter expert on conversational and generative AI technologies, providing strategic guidance, industry best practices, and solution recommendations.
- Lead the implementation, configuration, testing, validation, and optimization of AI\-powered capabilities, including Prompt\-Driven AI, Classifier\-Driven AI, AI Tags, Auto QM, Advanced Sentiment Models, Trending Topics, and related technologies.
- Assess customer processes, workflows, and operational practices to identify opportunities for automation, efficiency gains, quality improvement, and enhanced customer outcomes.
- Advise customers on AI adoption strategies, governance considerations, operational readiness, and emerging technology capabilities.
- Provide guidance on workforce optimization practices and customer experience improvement strategies when applicable.
Analytics \& Business Insights
- Analyze operational, customer experience, and solution performance data to identify trends, risks, opportunities, and strategic recommendations.
- Develop and deliver executive\-level reports, dashboards, presentations, business reviews, and value realization assessments that communicate business impact.
- Define, establish, and monitor key performance indicators (KPIs), benchmarks, and success measures aligned with customer objectives.
- Translate complex data into actionable insights that support decision\-making and drive continuous improvement.
Problem Resolution \& Cross\-Functional Collaboration
- Lead the investigation and resolution of complex business and technical issues through root cause analysis, risk assessment, and strategic problem\-solving.
- Serve as an escalation point for complex customer issues, ensuring timely resolution and maintaining customer confidence.
- Partner with Program Managers to align consulting activities with overall program objectives and customer success goals.
- Collaborate with Product Management, Engineering, Support, Customer Success, and Professional Services teams to address customer challenges and identify opportunities for product and process improvement.
- Provide consulting leadership and subject matter expertise across customer engagements and internal initiatives.
Customer Success \& Continuous Improvement
- Drive customer success through recommendations that increase adoption, utilization, and business value.
- Leverage customer feedback, performance data, and industry trends to optimize outcomes.
- Influence customer strategies through data\-driven insights and best practices.
- Document recommendations, engagement outcomes, and lessons learned to support knowledge sharing.
- Manage multiple customer engagements while maintaining high customer satisfaction.
- Contribute to consulting best practices, methodologies, and reusable assets.
Minimum Requirements:
- Bachelor's degree in Business, Computer Science, Information Systems, Engineering, or a related field, or an equivalent combination of education and practical experience.
- Minimum of 7 years of experience in customer\-facing consulting, solution consulting, professional services, technical consulting, application consulting, or related advisory roles.
- Demonstrated experience leading complex customer engagements and delivering successful business outcomes.
- Strong understanding of AI\-powered technologies, including prompt\-based AI, classifier\-based AI, conversational AI, and related automation technologies.
- Proven ability to act as a trusted advisor and influence stakeholders across multiple levels of an organization.
- Strong analytical, problem\-solving, and troubleshooting skills.
- Experience developing executive presentations, business reviews, reports, dashboards, and actionable business insights.
- Excellent verbal and written communication skills.
- Strong presentation, workshop facilitation, and stakeholder management skills.
- Ability to collaborate effectively across cross\-functional teams and influence without direct authority.
- Demonstrated ability to independently manage multiple priorities and customer engagements.
- Successful completion of a background screening process including, but not limited to, employment verifications, criminal search, OFAC, SS Verification, as well as credit and drug screening, where applicable and in accordance with federal and local regulations.
Preferred Requirements:
- Experience supporting contact center operations or customer experience environments.
- Experience with Workforce Management (WFM) solutions, preferably Calabrio or Verint Solution.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Consultant positions make up 0% of the market. At Verint Systems Inc., this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $152,604 based on 12 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Verint Systems Inc. AI Hiring
Verint Systems Inc. has 1 open AI role right now. They're hiring across AI Consultant. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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