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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, Professional Services is an embedded AI practitioner dedicated to advancing AI capabilities within the Professional Services (PS) organization. This role translates Agiloft's enterprise AI strategy into practical solutions that improve implementation efficiency, consultant productivity, and delivery automation.
Working exclusively on PS initiatives, the role builds AI\-powered workflows, agentic solutions, and automation that enhance customer implementations while operating within AI Operations governance. Day\-to\-day priorities are driven by PS leadership, with AI Operations providing standards, architecture, tooling, and strategic alignment. This role will require developing a deep understanding of how Professional Services currently delivers projects in order to help identify opportunities, shape strategy, and drive the adoption of AI to improve the efficiency, consistency, and quality of service delivery.
Reporting to Professional Services leadership with a dotted\-line relationship to the VP of AI Operations, this role serves as both a hands\-on builder and trusted AI advisor, partnering closely with PS AI Builders and AI Operations to deliver scalable, governed AI solutions.
### Job Responsibilities
AI Delivery
- Design, build, and maintain AI\-powered workflows, automations, and tooling that improve Professional Services delivery.
- Develop agentic workflows, prompt\-based tools, and lightweight AI applications using approved platforms.
- Translate delivery challenges into prioritized AI solutions in partnership with PS leadership.
- Coach and collaborate with PS AI Builders to improve AI delivery maturity.
- Monitor, test, document, and continuously improve deployed AI solutions.
- Build automation that reduces manual Agiloft configuration, testing, and validation effort.
- Evaluate emerging AI technologies and recommend adoption through AI Operations governance.
- Develop and deliver training programs to drive AI adoption across the Professional Services organization.
- Provide 1:1 coaching and hands\-on support to PS personnel to accelerate AI adoption, reinforce best practices, and address project\-specific needs.
Strategy \& Leadership
- Help define the AI roadmap for Professional Services.
- Serve as the primary AI advisor for PS delivery methodology and tooling.
- Represent Professional Services in cross\-functional AI initiatives.
- Share reusable patterns and best practices across the organization.
Governance \& Collaboration
- Ensure all AI solutions comply with AI Builders Program standards and governance policies.
- Register solutions in the AI Build Registry and follow established intake processes.
- Partner with AI Operations and the Principal Data \& Integrations Architect on architecture, data, and platform alignment.
- Communicate project status, risks, and opportunities with PS leadership and AI Operations.
- Other duties as assigned
### Required Qualifications
- Bachelor's degree in Computer Science, Information Systems, or equivalent experience.
- 2–4 years building AI, automation, or technical implementation solutions.
- Strong Python skills for automation, APIs, and AI workflows.
- Experience building agentic workflows using LLM frameworks (LangGraph, LangChain, Mastra, n8n, Tines, or similar).
- Experience with AI\-assisted development tools (Claude Code, OpenAI Codex, or similar).
- Knowledge of RAG, embeddings, vector databases, prompt engineering, tool use, and agent memory.
- Ability to independently design, build, and iterate AI solutions.
- Experience working across technical and business stakeholders.
- Strong documentation and governance discipline.
- SaaS industry experience required.
- Passion for applying AI to solve real business problems.
### Preferred Qualifications
- Professional Services or enterprise software implementation experience.
- Experience with highly configurable low\-code/no\-code platforms.
- Familiarity with CLM, LegalTech, or enterprise B2B SaaS.
- Experience in private equity\-backed SaaS companies.
- Knowledge of enterprise AI governance.
- Experience working with centralized AI organizations.
- Familiarity with Lovable, Retool, or similar low\-code platforms.
- Experience using Snowflake or comparable cloud data platforms.
- Interest in AI\-driven transformation of Professional Services delivery.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Agiloft AI Hiring
Agiloft has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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/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 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).
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 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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