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About This Role
Saviynt's AI\-powered identity platform manages and governs human and non\-human access to all of an organization's applications, data, and business processes. Customers trust Saviynt to safeguard their digital assets, drive operational efficiency, and reduce compliance costs. Built for the AI age, Saviynt is today helping organizations safely accelerate their deployment and usage of AI. Saviynt is recognized as the leader in identity security, with solutions that protect and empower the world’s leading brands, Fortune 500 companies and government institutions. For more information, please visit www.saviynt.com.
We’re looking for a Marketing AI Agent Engineer to help build, configure, and scale AI\-powered GTM workflows across Marketing, SDR, and Revenue Operations. This role will sit at the intersection of Marketing Operations, AI automation, data orchestration, and go\-to\-market execution.
This person will help turn our AI GTM strategy into working systems. That means building and managing AI agents that can research accounts, enrich leads, score fit, monitor buying signals, support SDR outreach, improve CRM data quality, and help activate audiences across email, ads, events, and partner motions.
Reporting into the Marketing Operations and AI Strategy organization, you will be a hands\-on builder responsible for connecting systems, designing agent workflows, improving automation quality, and helping Marketing move from manual execution to scalable AI\-assisted GTM operations.
### What You’ll Drive
- ### AI Agent Development \& Workflow Automation
+ Build, configure, test, and optimize AI agents that support Marketing and SDR workflows across prospecting, enrichment, scoring, routing, outreach, and reporting
+ Develop agentic workflows using tools such as Claude, Clay, MCP, Snowflake, Salesforce, HubSpot, Outreach, Gong, and related GTM systems
+ Translate business requirements into structured AI workflows that can operate reliably, repeatably, and at scale
+ Build agents that monitor key GTM signals such as job changes, hiring velocity, funding events, web intent, content engagement, and account fit
+ Create workflows that reduce manual research and give SDRs better prioritized accounts, stronger contact context, and cleaner outreach hooks### GTM Signal Intelligence \& Prospecting Automation
+ Help build and scale autonomous prospecting motions that identify ICP\-fit accounts and contacts based on real\-time buying signals
+ Support ICP fitness scoring workflows that assess inbound leads, event leads, partner leads, and prospecting lists before they move into downstream systems
+ Build logic that blends signal\-based prospecting with broader whitespace nurture motions
+ Develop workflows that help SDRs prioritize the right accounts at the right time with the right message
+ Partner with Marketing, SDR, and RevOps teams to ensure AI\-driven prospecting aligns with pipeline goals, territory strategy, and campaign priorities### Data Orchestration \& System Connectivity
+ Connect and normalize data across Salesforce, HubSpot, Clay, Snowflake, Outreach, Gong, LinkedIn Ads, HockeyStack, and other GTM platforms
+ Build structured inputs, data models, prompts, API workflows, and JSON\-based logic that allow AI agents to take action with clean context
+ Support audience segmentation workflows across cold, warm, and hot account/contact cohorts
+ Help operationalize Snowflake and other data sources as audience and intelligence layers for AI\-powered GTM activation
+ Partner with Marketing Operations, RevOps, Data, and IT teams to make sure agent workflows are secure, governed, and aligned with system architecture### CRM Hygiene \& Data Quality Agents
+ Build and maintain AI\-powered data quality workflows that improve the accuracy of CRM and marketing data
+ Support agents for employment verification, LinkedIn URL enrichment, region/location detection, account/contact enrichment, and stale data detection
+ Design workflows that catch bad records before SDRs waste time on outdated contacts or inaccurate account information
+ Improve the quality of downstream workflows by ensuring agent decisions are based on clean, current, and complete data
+ Partner with Salesforce and HubSpot system owners to write back useful intelligence in a structured and governed way### AI\-Powered SDR \& Marketing Activation
+ Build workflows that support AI\-generated email sequences, personalized outreach hooks, account research summaries, and meeting preparation briefs
+ Help connect outbound email, LinkedIn Ads, website intent, events, and partner motions into a more unified activation model
+ Support agent workflows for event and user group planning, including geographic intelligence based on pipeline, whitespace, and ICP contact density
+ Partner with SDR leadership to improve rep efficiency, reduce manual research, and increase time spent on high\-value conversations
+ Help create feedback loops that improve messaging, scoring, segmentation, and workflow performance over time### AI Governance, Testing \& Optimization
+ Test agent outputs for accuracy, consistency, brand alignment, and business usefulness
+ Build repeatable QA processes for prompts, structured inputs, workflow logic, and agent\-generated outputs
+ Identify where human review is required versus where automation can safely run independently
+ Monitor workflow performance and recommend improvements based on adoption, output quality, conversion impact, and operational efficiency
+ Partner with Security, IT, and Operations teams to ensure AI workflows follow internal governance and data handling standards
### WHAT YOU BRING
- ### Must\-Have
+ 4\+ years of experience in Marketing Operations, Revenue Operations, GTM Systems, AI Operations, Marketing Automation, or a related technical GTM role
+ Hands\-on experience building workflows across GTM systems such as Salesforce, HubSpot, Clay, Outreach, Gong, LinkedIn Ads, Snowflake, or similar platforms
+ Strong understanding of B2B SaaS funnel motions, including lead routing, SDR workflows, campaign attribution, pipeline creation, and account\-based marketing
+ Experience working with AI tools such as Claude, ChatGPT, Cursor, Claygent, or similar AI workflow platforms
+ Ability to build structured prompts, workflow logic, decision trees, and reusable AI instructions that produce consistent outputs
+ Familiarity with APIs, webhooks, JSON, data enrichment, field mapping, and cross\-system automation
+ Strong understanding of CRM data structure, including leads, contacts, accounts, campaigns, opportunities, activities, and custom fields
+ Ability to translate business problems into practical technical workflows without over\-engineering the solution
+ Strong analytical and troubleshooting mindset with the ability to diagnose workflow failures, data issues, and process gaps
+ Comfortable working cross\-functionally with Marketing, SDR, RevOps, Sales, Data, IT, and Security stakeholders### Nice\-to\-Have
+ Experience with Claude Managed Agents, MCP, Clay, Snowflake, HockeyStack, Gong, Outreach, or similar AI\-native GTM tools
+ Experience building AI agents for SDR prospecting, lead enrichment, account research, CRM hygiene, or outbound personalization
+ Familiarity with Python, SQL, JavaScript, or low\-code automation platforms
+ Experience with vector databases, RAG workflows, Pinecone, or structured knowledge retrieval
+ Experience with Salesforce administration, HubSpot operations, campaign operations, or marketing automation architecture
+ Experience supporting ABM, intent\-based marketing, paid audience sync, or multi\-channel GTM activation
+ Experience in cybersecurity, identity security, enterprise SaaS, or another complex B2B selling environment
+ Comfort working in a fast\-moving environment where AI capabilities, tools, and internal processes are evolving quicklyYou’re the Type of Person Who
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+ Thinks like a systems builder, not just a task executor
+ Gets excited about turning messy manual processes into scalable AI workflows
+ Understands that great AI output depends on clean data, clear instructions, and strong process design
+ Can build fast, test carefully, and improve continuously
+ Knows when automation should act independently and when a human needs to stay in the loop
+ Is comfortable working in the gray area between business strategy and technical execution
+ Brings practical ideas, not science projects
+ Can explain technical concepts clearly to non\-technical stakeholders
+ Wants to build the operating layer for the next generation of GTM executionWhy This Role Matters
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+ You will help build the AI operating system for Marketing and SDR execution
+ You will reduce manual research, improve lead quality, and help SDRs focus on the highest\-value conversations
+ You will improve how Saviynt captures, scores, enriches, and activates GTM signals
+ You will help connect AI, data, systems, and human execution into one scalable GTM motion
+ You will play a key role in moving Marketing Operations from workflow support to AI\-powered revenue acceleration
If required for this role, you will:* Complete security \& privacy literacy and awareness training during onboarding and annually thereafter
- Review (initially and annually thereafter), understand, and adhere to Information Security/Privacy Policies and Procedures such as (but not limited to):
\> Data Classification, Retention \& Handling Policy
\> Incident Response Policy/Procedures
\> Business Continuity/Disaster Recovery Policy/Procedures
\> Mobile Device Policy
\> Account Management Policy
\> Access Control Policy
\> Personnel Security Policy
\> Privacy Policy
Saviynt is an amazing place to work. We are a high\-growth, Platform as a Service company focused on Identity Authority to power and protect the world at work. You will experience tremendous growth and learning opportunities through challenging yet rewarding work which directly impacts our customers, all within a welcoming and positive work environment. If you're resilient and enjoy working in a dynamic environment you belong with us! *Saviynt is an equal opportunity employer and we welcome everyone to our team. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.*
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
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Saviynt, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Saviynt AI Hiring
Saviynt has 3 open AI roles right now. They're hiring across AI Agent Developer, AI Architect, AI Software Engineer. Positions span Remote, US, Milpitas, CA, US. Compensation range: $225K - $225K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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.
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