Interested in this AI/ML Engineer role at eGain?
Apply Now →About This Role
eGain (NASDAQ: EGAN) is the global leader in AI knowledge management platforms for enterprise customer engagement. Trusted by the world’s most customer\-centric companies, eGain’s platform combines the power of generative AI with structured, curated enterprise knowledge to deliver accurate, consistent, and compliant answers across every customer touchpoint.
eGain has a long\-standing OEM partnership with Cisco, embedding our chat and email management products inside worldwide deployments of Cisco’s enterprise contact center platform. That installed base is a built\-in, high\-intent market: these are enterprises that have already trusted Cisco’s contact center for their operations and, through the OEM relationship, already run eGain technology under the hood.
### The Opportunity
We’re hiring a Sales Executive to go direct into this Cisco contact center customer base and extend this OEM presence with an eGain branded AI Knowledge relationship, driving service automation and agent assist, powered by trusted knowledge from eGain. This is a hunting role with a warm start: the technical foothold exists, and the job is to build and grow the commercial relationship.
### What You Will Do
#### Account \& Pipeline Development
- Sell into eGain’s Cisco OEM installed base and build a systematic outbound motion into those accounts.
- Build and manage a pipeline from first outreach through discovery, solution positioning, business case, procurement, and close.
#### Solution Positioning \& Value Selling
- Identify contact center leaders (CX, service operations, IT) at these enterprises and position AI Knowledge as the layer that effortlessly enhances their existing Cisco environment, driving self\-service deflection, agent assist, and faster resolution.
- Develop and pitch value narratives that quantify the ROI of AI\-driven service automation and agent assist (deflection rates, handle time, first\-contact resolution, CSAT).
#### Partner \& Alliance Engagement
- Partner closely with eGain’s Cisco alliance team, solution consultants, and customer success to design account plans and land expansion deals inside existing Cisco relationships.
- Represent eGain at Cisco partner and field events, and build relationships with Cisco account teams and channel partners (e.g., CDW, WWT) who influence these accounts.
#### Forecasting \& Pipeline Hygiene
- Maintain accurate forecasting and pipeline hygiene in the CRM, and report on territory coverage and account penetration.
### What You Bring
#### Required Experience
- 5–10 years of quota\-carrying enterprise software or contact center technology sales experience.
- Direct experience selling within the Cisco ecosystem, either at Cisco itself (contact center / collaboration business unit) or at a major Cisco VAR/SI partner, with working knowledge of how Cisco contact center deals get sourced, structured, and closed.
- A track record of prospecting and closing net\-new or expansion business within a defined account base, not just managing inbound demand.
- Comfort selling to both business buyers (CX, operations) and technical buyers (IT, contact center architecture).
- Strong consultative selling skills, able to build a business case grounded in operational metrics, not just product features.
#### Preferred Experience
- Familiarity with AI/knowledge management, self\-service, or conversational AI solutions.
### Why eGain
- Recognized as a Leader in Gartner’s inaugural Magic Quadrant for Customer Service Knowledge Management Systems.
- 25\+ years of experience helping the world’s largest enterprises, including JPMorgan Chase, Liberty Mutual, BUPA, and the IRS, deliver trusted, AI\-powered knowledge to customers and agents.
- A built\-in, high\-intent market: this role goes direct into enterprises that already trust and run eGain technology through our OEM partnership with Cisco.
- Sits at the center of one of eGain’s fastest\-growing go\-to\-market motions: converting a deeply embedded technology footprint into expanded, high\-value AI Knowledge relationships.
### Our Hiring Process is “Easy with eGain”
### Step 1
Written test
- Aptitude section – this is a GRE style test (60 minutes or less)
- Functional section – this is a take\-home test
### Step 2
Panel interview
### Next step
Email your resumé to [email protected] with the position title “Sales Executive” in the email subject.
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 eGain, 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 $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.
eGain AI Hiring
eGain has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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