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About This Role
About Egen:
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
Want to learn more about life at Egen? Check out these resources in addition to the job description.
- > Meet Egen
- > Life at Egen
- > Culture and Values at Egen
- > Career Development at Egen
Role Overview:
Egen is looking for a strategic and client-facing Senior Consultant, Agentic Change Enablement to lead high-impact transformations. This role serves as the human-side anchor on engagements where technology is only part of the story. You will embed with client project teams to ensure employees at every level are prepared, equipped, and motivated to adopt new ways of working powered by Google Cloud, Gemini, and the emerging "agentic work" paradigm. Reporting to the Senior Director, you will be responsible for activating and scaling our delivery playbook to foster deep organizational alignment and tangible business outcomes.
About the job:
1. Agentic Change Strategy & Planning
- Framework Application: Apply structured change management methodologies (e.g., Prosci/ADKAR, Kotter) to design tailored strategies for complex AI and Workspace transformations.
- Discovery & Assessment: Facilitate Gemini Enterprise Discovery Workshops and conduct change impact analyses to identify risks, gaps, and "agentic" opportunities.
- Outcome Definition: Define "North Star" outcomes and success metrics (KPIs) to track adoption progress and organizational health.
- Impact Mapping: Document key policy and process impact changes resulting from the integration of AI into daily operating rhythms.
2. Stakeholder Engagement & Executive Advisory
- Executive Coaching: Support and engage senior leaders and executive sponsors, coaching them on their visible change leadership role.
- Resistance Management: Anticipate and proactively manage resistance to AI, developing targeted mitigation strategies for high-risk stakeholder groups.
- Champion Activation: Build and activate "Gemini Captain" (Change Champion) networks to amplify adoption at scale through peer-to-peer learning.
- Translation: Serve as a natural "bridge," translating complex technical AI concepts into compelling business value and strategic imperatives.
3. Training, Education & Communication
- Curriculum Design: Develop persona-based learning journeys and role-based training programs that build competency in agentic solutions.
- Narrative Development: Create compelling change narratives that frame AI-driven efficiencies as an opportunity for workforce evolution, not replacement.
- Asset Creation: Leverage Google Workspace tools (Slides, Sites, Docs, Vids) to produce high-quality enablement assets, including leader toolkits and prompt libraries.
- Workshop Facilitation: Design and lead "Art of the Possible" kickoffs and dynamic design-thinking sessions using tools like Miro or Mural.
4. Adoption Measurement & Sustainment
- Pilot Implementation: Lead Gemini Enterprise Pilot programs, managing Vanguard Cohorts and capturing baseline productivity reports.
- Support Infrastructure: Establish multi-layered support systems, including Slack/Teams channels, office hours, and deployed self-service support agents.
- Sustainment: Conduct post-go-live reinforcement activities and pulse check surveys to embed new behaviors permanently.
- Reporting: Generate weekly usage and adoption reports for project stakeholders and client leadership.
About you:
Required
- Experience: 3–6+ years in organizational change management or management consulting, with a focus on enterprise-scale technology programs.
- Methodology: Proven proficiency with change management frameworks; Prosci (ADKAR) certification is strongly preferred.
- Technology Background: Experience supporting cloud or SaaS adoption programs (Google Workspace, Google Cloud, or equivalent).
- Communication: Exceptional skills in crafting narratives and presenting to everyone from end users to C-suite executives.
- Engagement: Strong stakeholder management capabilities with experience influencing across complex organizations.
- Education: Bachelor's degree in Organizational Development, Business, Psychology, Communications, or a related field.
Preferred
- AI Expertise: Hands-on experience with Generative AI adoption (e.g., Gemini for Google Workspace or Vertex AI).
- Partner Experience: Prior experience at a Google Cloud Premier Partner or technology consulting firm.
- Technical Literacy: Ability to collaborate with technical workstreams (engineers, data scientists) to translate technical change into human-centered action.
- Measurement: Experience building adoption dashboards and reporting metrics to leadership.
- Workshop Mastery: Extensive experience running dynamic strategy workshops both in-person and with tools like Miro or Mural.
$135,400 - $186,358 a year This position may be hired at multiple levels. Final leveling is determined during the interview process based on a candidate's experience, skills, and interview outcomes, and the applicable salary range will align with the final level assigned.
Compensation is determined based on factors including experience, expertise, interview performance, and geographic location, where applicable. Compensation & Benefits:
This role is eligible for our competitive salary and comprehensive benefits package to support your well-being:
- Comprehensive Health Insurance
- Paid Leave (Vacation/PTO)
- Paid Holidays
- Sick Leave
- Parental Leave
- Bereavement Leave
- 401 (k) Employer Match
- Employee Referral Bonuses
Check out our complete list of benefits here - >https://egen.ai/people/#benefits
Important:All roles are subject to standard hiring verification practices, which may include background checks, employment verification, and other relevant checks.
EEO and Accommodations:
Egen is an equal opportunity employer and is committed to inclusion, diversity, and equity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veterans’ status, or any other characteristic protected by federal, state, or local laws. Egen will also consider qualified applications with criminal histories, consistent with legal requirements. Egen welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of the selection process. Please advise the talent acquisition team if you require accommodations during the interview process. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $135K-$186K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Egen Solutions Inc, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($160K) sits 24% below the category median. Disclosed range: $135K to $186K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Egen Solutions Inc AI Hiring
Egen Solutions Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $186K - $265K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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