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About This Role
SENIOR CONSULTANT, PROFESSIONAL SERVICES – AI SERVICESUS REMOTE EGNYTE YOUR CAREER. SPARK YOUR PASSION.
Egnyte is a place where we spark opportunities for amazing people. We believe that every role has a great impact, and every Egnyter should be respected. When joining Egnyte, you’re not just landing a new career, you become part of a team of Egnyters that are doers, thinkers, and collaborators who embrace and live by our values:
- Invested Relationships
- Fiscal Prudence
- Candid Conversations
ABOUT EGNYTE
Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere. For more information, visit www.egnyte.com.
Our Global Technology Solutions team is looking for a Senior Consultant, Professional Services - AI Services, who will work on high-impact engagements in support of our customers within the Egnyte PS AI Services practice. As a Senior Consultant on the team, you will be responsible for building custom solutions and agents that integrate with customer data and workflows as well as internal and external AI models via MCP to unlock the full power of Egnyte’s AI-first platform for our customers.
The ideal candidate is a hands-on technical consultant and strong communicator, who can quickly understand customer use cases and deliver meaningful business value through AI-centric solutions. This is an extremely fast-paced role at the center of innovation for our team.
WHAT YOU’LL DO:
- Work with Egnyte’s customers to build custom solutions and workflows leveraging AI agents and Egnyte’s rich AI platform capabilities, as well as MCP integrations. In addition, guide customers with custom prompts designed to maximize business value. Deliver solutions on-time, and with high quality.
- Engage directly with customers during pre-sales architecture reviews and throughout the engagement lifecycle to understand requirements and shape solution designs that meet their use cases and deliver true business value.
- Stay current with the rapidly evolving AI landscape that includes foundation models, agentic frameworks, and emerging MCP standards. Translate that knowledge into actionable guidance and solutions for customers.
- Collaborate with Egnyte Sales, Product, and Customer Success teams to identify expansion opportunities, provide technical expertise, and ensure customer value realization.
- Establish and document best practices, delivery frameworks, and reusable solution patterns for the AI Technical practice
- Assess and advise customers on architecture patterns that best complement Egnyte’s platform.
YOUR QUALIFICATIONS:
- 7+ years of overall experience, with at least 3 years in a customer-facing Technology Consulting or Professional Services role.
- 3+ years of hands-on experience with state-of-the-art AI technologies, including large language models, agentic AI frameworks, and prompt engineering methodologies.
- Demonstrated experience with MCP (Model Context Protocol) integrations and agentic AI architectures; ability to advise customers and build working solutions in this space.
- Experience with Cloud platforms (AWS, Azure, or Google Cloud) and modern SaaS integration patterns; cloud certifications are a plus.
- Strong understanding of data privacy, security, and governance considerations as they apply to AI and custom development solutions in enterprise environments.
- Demonstrated experience engaging large enterprise customers as well as mid-market and SMB accounts; industry experience in AEC, Life Sciences/Biotech, or FinTech is a plus.
- Excellent communicator with the ability to translate complex technical concepts for business stakeholders and to build trust with senior customer decision-makers.
- Record of consistently meeting delivery objectives and expanding customer relationships at high-growth technology companies.
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
COMPENSATION:
- Our compensation reflects the cost of labor across multiple U.S. geographic locations and pay varies based on defined markets. The standard base pay range for this position across the U.S. is $125k to $140k annually. Pay varies by work location and may also be dependent on job-related skills, knowledge, and/or experience. During the interview and/or hiring process, your recruiter can share more information about the compensation package specific to the role and job location.
BENEFITS:
- Competitive salaries and comprehensive benefits
- Company equity depending on role and level
- Flexible hours and generous time off (RTO, Responsible Time Off) to help support your work-life balance.
- Paid holidays and sick time
- 401(k) with company match
- Health Savings Account (HSA) with a generous employer contribution and Flexible Spending Account (FSA) options
- Up to 12wks of paid Parental and 10wks Adoption Leave to help you grow your family
- Modern and collaborative offices located in Draper, UT; Raleigh, NC; Mountain View, CA; Reading, England, and Poznan, Poland
- Gym, cell phone, and internet reimbursement
- Free well-being apps such as Spring Health for Guardian are offered, as well access to our Employee Assistance Program (EAP)
- Perks include discounted pet insurance, electronics, theme park tickets, travel, plus more
- Your own Egnyte account with lifetime access
- HealthJoy – a benefits navigation app that lets you access your benefits and get answers to your questions all in one place
- One Medical virtual care, providing you with healthcare access across the country
Equal Employment Opportunity
Egnyte, Inc. is an Equal Opportunity Employer that does not discriminate on the basis of actual or perceived race, color, creed, religion, national origin, ancestry, citizenship status, age, sex or gender (including pregnancy, childbirth, pregnancy-related conditions, and lactation), gender identity or expression (including transgender status), sexual orientation, marital status, military service and veteran status, physical or mental disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances. Egnyte, Inc.'s management team is dedicated to this policy with respect to recruitment, hiring, placement, promotion, transfer, training, compensation, benefits, employee activities, access to facilities and programs, and general treatment during employment.
At Egnyte, we embrace our unique differences and thrive on the individuality of our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and foster connectedness across our varied workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.
Any employees with questions or concerns about equal employment opportunities in the workplace are encouraged to bring these issues to the attention of [email protected]. Egnyte, Inc. will not allow any form of retaliation against employees who raise issues of equal employment opportunity. If employees feel they have been subjected to any such retaliation, they should contact [email protected]. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.
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Salary Context
This $125K-$140K range is in the lower quartile 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 Egnyte, 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 ($132K) sits 38% below the category median. Disclosed range: $125K to $140K.
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.
Egnyte AI Hiring
Egnyte has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $140K - $140K.
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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