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About This Role
As the most trusted global leader in data-first contract lifecycle management (CLM) software, Agiloft helps organizations manage the end-to-end process of proposing, negotiating, signing, and leveraging contracts using our flexible Data-first Agreement Platform (DAP). With contract data as the foundation, customers quickly and collaboratively reach agreement and leverage contract visibility to thrive with competitive advantage. Employing powerful, pragmatic artificial intelligence as a legal force multiplier, and robust integration capabilities as a data liberator, organizations around the world trust Agiloft’s certified implementers to deliver connected, intelligent, and autonomous solutions across the entire contract lifecycle.
Top analysts like Gartner, Forrester, and IDC agree, all showing Agiloft as a leader in the CLM space. Our no code platform is easily managed and administered by business users, which is why Agiloft is the contract you keep: nearly a full 100% of new customers are satisfied with their initial implementations, and some 97% of customers renew every year. Ours is a growing, vibrant, successful company that is at the forefront of a market that is becoming a must-have for all organizations.
We believe that the way to build the strongest, most vibrant place to work is to bring in individuals from all walks of life, and to support them in bringing their authentic selves to their day, every day. Our working philosophy is that “EX = CX”: when employee experience is excellent, so is customer experience. We support multiple Employee Resource Groups (ERGs), and offer a working environment that supports healthy work/life balance, including floating holidays and a quarterly, no-questions-asked wellness day.
Position Overview
The AI Ops Engineer, GTM is Agiloft's dedicated AI practitioner for the go-to-market organization, shaping and executing the AI vision across Sales, Marketing, Customer Success, Revenue Operations, and the broader revenue function. Equal parts strategist and builder, this person understands how GTM teams win, where they lose time, and where AI can create meaningful leverage.
Customer and market proximity is a core requirement. The role stays actively involved with customers and the sales process to understand what it takes to sell Agiloft, retain customers, and grow accounts, then uses that insight to experiment, measure outcomes, and scale successful AI-powered GTM patterns.
Reporting to the SVP GTM Intelligence with a dotted line to the VP AI Operations, this role partners with GTM leadership for strategic direction while AI Operations provides architectural standards, governance, tooling access, and organizational alignment. Working with the GTM AI Builders community, the AI Ops Engineer prioritizes and delivers solutions that move the revenue needle while continuously developing AI engineering skills and helping transform how Agiloft goes to market.
Job Responsibilities
AI Vision and GTM Strategy
- Serve as the primary AI thought leader for GTM, helping Sales, Marketing, Customer Success, and Revenue Operations identify and prioritize the highest-value opportunities to eliminate friction, accelerate revenue, improve customer experience, and increase seller productivity.
- Develop and maintain a GTM-specific AI roadmap aligned with the broader AI Operations strategy, balancing near-term workflow optimization with longer-term transformation and sequencing investments against the highest-impact pain points.
- Maintain active involvement with customers and the sales process to ground AI priorities in market realities and identify moments where AI can change outcomes for both the team and the buyer.
- Contribute the GTM perspective to Agiloft's external AI narrative and thought leadership, and measure initiative impact through adoption, productivity, pipeline influence, customer outcomes, and revenue metrics.
AI Execution and Workflow Optimization
- Design, scope, and build AI copilots, agents, retrieval-augmented workflows, and human-in-the-loop automations that augment—not replace—GTM teams across use cases such as pipeline management, outbound sequences, customer health, content generation, meeting intelligence, and contract and renewal triggers.
- Own end-to-end delivery of large-scale GTM AI solutions from concept to production, coordinating with GTM AI Builders, AI Operations, and technical stakeholders.
- Partner with the GTM AI Builders community to advance build maturity through coaching, co-build support, and review of department-originated AI work.
- Monitor deployed tools and automations, iterating based on adoption data, team feedback, and changing business needs; maintain testing, documentation, and handoff standards for continuity and auditability.
- Evaluate GTM-relevant AI tooling and bring recommendations to AI Operations for platform alignment before adoption.
Continuous Experimentation, Development and Alignment
- Continuously develop AI engineering skills and stay current on agentic workflows, LLM integrations, automation platforms, and AI-assisted coding and scripting.
- Operate within the AI Builders Program at the L4 AI Ops level for GTM-scoped work, collaborating with L3 Practitioners; register builds in the AI Build Registry and comply with POL-AIOPS-001 and related governance policies.
- Maintain clear communication with the VP AI Operations and GTM leadership on build status, opportunities, blockers, and priorities; coordinate with the Principal Data and Integrations Architect when use cases require data pipelines, Snowflake access, or warehouse connectivity.
- Coach GTM teams on responsible AI adoption and help increase AI fluency across the organization.
- Other duties as assigned.
Required Qualifications
- Bachelor's degree in Business, Marketing, Communications, Information Systems, a related field, or equivalent practical experience.
- Experience in a GTM, revenue operations, or customer-facing role at a B2B SaaS company, with exposure to the full revenue cycle from pipeline to renewal.
- Experience with CRM, marketing automation, and CS platforms such as Salesforce, HubSpot, Gainsight, or Outreach as an admin and/or power user or AI workflow integration target.
- 1 to 3 years of direct experience leading AI optimization of GTM workflows, with demonstrated results across sales, marketing, customer success, or revenue operations and examples of initiatives conceived and driven to completion.
- Base-level Python scripting skills sufficient to read, modify, and execute automation scripts, plus the aptitude and commitment to deepen scripting, AI engineering, and architectural fluency through structured learning.
- Foundational knowledge of RAG, embeddings, prompt engineering, agentic workflows, and how LLM-powered tools are built and integrated; technical aptitude to operate as an L4 AI Builder alongside AI Operations.
- Familiarity with AI workflow orchestration platforms such as n8n, Tines, LangChain, or equivalents, including connecting AI tools to GTM systems of record; familiarity with AI-assisted coding tools such as Claude Code or OpenAI Codex.
- Strong customer orientation and experience engaging customers, understanding the buying journey, and using that knowledge to identify AI leverage in the GTM motion.
- Ability to work as a credible strategic partner to senior GTM leaders, translate AI capabilities into business outcomes, and collaborate respectfully across GTM functions and AI Operations.
- Genuine passion for practical AI applications in revenue contexts, with curiosity and self-directed learning that support continuous skill development.
- SaaS industry experience required.
Preferred Qualifications
- Familiarity with CLM, legal tech, eDiscovery, or adjacent enterprise software serving legal, procurement, or finance buyers.
- Exposure to enterprise AI governance practices, including policy-based controls, intake processes, and AI build registries, and experience collaborating with a centralized AI or data operations function.
- Familiarity with Snowflake or equivalent cloud data platforms as a consumer of data infrastructure for AI use cases.
- Experience participating in customer events, industry conferences, or field sales activities that deepen understanding of buyer behavior and the competitive landscape.
- Experience in a private equity-backed SaaS organization.
Ensuring a diverse and inclusive workplace is our priority. We are committed to an environment of acceptance where you are free to bring your full self to work. All employment decisions at Agiloft are based on business needs, job requirements, and individual qualifications without regard to race, color, religion or belief, national or social ethnic origin, sex, age, sexual orientation, gender identity and/or expression, parental status, marital status, Veteran status, or any other status protected by the laws or regulations in the locations where we operate. If you have a need that requires accommodation during the recruiting process, please let us know by contacting Director, Talent Acquisition, Brad Toothman at [email protected].
Applicants from underrepresented groups such as minorities, veterans, or individuals with disabilities encouraged to apply.
Applications will be reviewed as submitted. There will be no application deadline for this opportunity. 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.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Agiloft, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Agiloft AI Hiring
Agiloft has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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