Interested in this AI Consultant role at Veeva Systems?
Apply Now →About This Role
Veeva Systems is a mission\-driven organization and pioneer in industry cloud, helping life sciences companies bring therapies to patients faster. As one of the fastest\-growing SaaS companies in history, we surpassed $3B in revenue in our last fiscal year with extensive growth potential ahead.
At the heart of Veeva are our values: Do the Right Thing, Customer Success, Employee Success, and Speed. We're not just any public company – we made history in 2021 by becoming a public benefit corporation (PBC), legally bound to balancing the interests of customers, employees, society, and investors.
As a Work Anywhere company, we support your flexibility to work from home or in the office, so you can thrive in your ideal environment.
Join us in transforming the life sciences industry, committed to making a positive impact on its customers, employees, and communities.### The Role
Veeva is looking for an AI\-fluent Implementation Consultant to join our Professional Services team, focusing specifically on our Vault Safety solution. As Veeva integrates advanced AI capabilities and autonomous agents into Vault Safety to transform case processing and drug safety workflows, we need innovative thinkers who can help life sciences organizations adopt AI safely, effectively, and compliantly.
In this role, you will serve as a trusted advisor, leading customers on how to implement, validate, govern, and monitor AI agents in a high\-stakes, regulated domain. While you will bring proven AI enablement expertise—whether from freelance AI strategy or cross\-industry AI consulting—you will rapidly build domain expertise in pharmacovigilance and learn the regulatory constraints (such as GxP, FDA, and EMA guidelines) governing drug safety. Prior experience navigating regulated environments (e.g., healthcare, financial services, aerospace, or life sciences) will be critical to your success in translating AI innovation into compliant business practices.
This is a remote, full\-time permanent role with Veeva. This is a customer\-facing role and we have no work location requirement if you are in close proximity to an airport and able to meet travel requirements. Veeva Systems does not anticipate providing sponsorship for employment visa status (e.g., H\-1B, OPT) for this employment position.
### What You'll Do
- Advise drug safety and pharmacovigilance clients on AI strategy, guiding them on how to establish governance and continuous oversight for AI agents within Vault Safety workflows
- Partner with clients to establish validation and testing frameworks for AI agents and features, ensuring compliance with strict computer system validation (CSV) and GxP standards
- Design and build tailored metrics, reporting, and oversight dashboards to continuously monitor AI agent accuracy, throughput, and performance in safety case management
- Rapidly learn and master Vault Safety product capabilities and core pharmacovigilance regulatory constraints to effectively bridge complex AI concepts with domain\-specific compliance needs
- Execute workshops and lead customer discussions focused on safety process optimization, AI adoption, requirements gathering, and system configuration
- Lead gap analyses, solution design, and customer training, acting as the primary consultant for AI\-driven Vault Safety implementations
- Ensure overall customer success and value realization throughout the entire implementation lifecycle
- Be a leader. Look for opportunities for our team and clients to innovate, keep up with the latest software, machine learning, manufacturing industries trends, and distill them into actionable tasks and solutions
### Requirements
- 3\+ years of experience in strategic or implementation consulting, helping clients adopt and operationalize AI solutions (experience as a freelance AI strategist or leading cross\-industry AI transformations is highly valued)
- Regulated Industry Experience: Proven experience consulting or working within a strictly regulated environment (e.g., Life Sciences, Healthcare, Finance, Insurance, Aerospace) where compliance, auditability, and governance are critical
- AI Governance \& Monitoring: Hands\-on experience designing governance models, agent oversight protocols, and performance metrics/dashboards to track AI reliability and ROI
- Domain Adaptability: Demonstrated ability to quickly learn complex industry domains, workflows, and regulatory frameworks (willingness and aptitude to rapidly master Pharmacovigilance is required)
- Technical \& Solution Agility: Ability to act with speed to understand complex business requirements, design corresponding software solutions, and "roll up your sleeves" to execute system configurations
- Stakeholder Management: Exceptional communication and consulting skills, with the ability to build trust and explain complex AI and validation topics to both business leaders and technical teams
- Travel: Ability to travel up to 50%. Usually much less
### Nice to Have
- Knowledge of Drug Safety and Pharmacovigilance processes and regulations
- Experience supporting high\-impact global system implementation programs as a consultant, business, or IT lead, and/or business sponsor
- Direct experience with systems such as Oracle Argus, ARISg, and/or other drug safety applications, including adjacent solutions for Signal Detection and/or Analytical Reporting
- Life science, computer science, or related degree
- SaaS/Cloud experience
- Experience in services delivery management and/or systems implementation
- Locality to major life sciences customer hub (New Jersey; Boston, MA; San Francisco, CA; San Diego, CA; Philadelphia, PA; Chicago, IL; RTP, NC; Indianapolis, IN; Columbus, OH)
### Interviewing with Veeva
We value your time and believe in a transparent hiring process. Here is the process you can expect.
- Follow the application process and submit your resume.
- Within 3 days, you will receive a link to a personality assessment administered by a third party.
- Once you complete the assessment, our team will review your full application package and follow up via email with our decision.
- If moving to the interview stage, the process is as follows:
+ A conversation with the hiring manager
+ A practical case exercise
+ A final conversation with our group's Senior Leader.
- Once all interviews are complete, the manager will be in touch with a final decision.
### Perks \& Benefits
- Medical, dental, vision, and basic life insurance
- Flexible PTO and company paid holidays
- Retirement programs
- 1% charitable giving program
### Compensation
- Base pay: $70,000 \- $140,000
- The salary range listed here has been provided to comply with local regulations and represents a potential base salary range for this role. Please note that actual salaries may vary within the range above or below, depending on experience and location. We look at compensation for each individual and base our offer on your unique qualifications, experience, and expected contributions. This position may also be eligible for other types of compensation in addition to base salary, such as variable bonus and/or stock bonus.
###### \#LI\-Remote
###### \#LI\-Associate
Veeva’s headquarters is located in the San Francisco Bay Area with offices in more than 15 countries around the world.
Veeva is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity or expression, religion, national origin or ancestry, age, disability, marital status, pregnancy, protected veteran status, protected genetic information, political affiliation, or any other characteristics protected by local laws, regulations, or ordinances. If you need assistance or accommodation due to a disability or special need when applying for a role or in our recruitment process, please contact us at talent\[email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. 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.
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 4,317 AI roles we're tracking, AI Consultant positions make up 0% of the market. At Veeva Systems, 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 $145,144 based on 14 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($105K) sits 28% below the category median. Disclosed range: $70K to $140K.
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.
Veeva Systems AI Hiring
Veeva Systems has 4 open AI roles right now. They're hiring across AI Agent Developer, AI Product Manager, AI Consultant. Based in Remote, US. Compensation range: $140K - $175K.
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 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 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).
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 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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