Interested in this AI/ML Engineer role at Ncontracts?
Apply Now →Skills & Technologies
About This Role
WHO WE ARE
Headquartered in Nashville, Tennessee, Ncontracts leads the industry in integrated risk management and compliance solutions, serving over 5,000 financial institutions nationwide. As a seven\-time Inc. 5000 Fastest Growing Companies honoree and consistent year\-over\-year recipient of "Best Places to Work" awards, we offer a thriving, work environment where career growth and life\-work balance go hand in hand.
At Ncontracts, you'll join a team of industry experts dedicated to strengthening the financial services sector through innovation and thought leadership. We're seeking creative, collaborative, and self\-driven professionals across all areas of our business \- from developing cutting\-edge solutions to sales, marketing, customer support, and beyond. Join us in our mission to make the financial industry stronger and more resilient, while advancing your career in a supportive, dynamic environment that values your unique skills and perspectives.
Position Overview
The AI Automation \& Enablement Specialist is responsible for driving operational efficiency, AI adoption, and systems integration across the Client Services organization at Ncontracts. This role sits at the intersection of technical execution and strategic enablement – building the automations, data pipelines, and AI\-powered workflows that eliminate process inconsistencies, reduce manual effort, and allow CS leadership and the broader team to operate with greater speed and clarity.
The role begins with CS leadership – identifying and resolving the manual, repetitive, and inconsistent workflows that create friction and slow decision\-making – and then scales proven solutions, tools, and capabilities across the broader Client Services organization. This is not a traditional IT or data engineering position. It is a hybrid role: part technical builder, part internal AI coach, and part systems architect, with a mandate to continuously improve how the CS organization operates.
The ideal candidate brings both technical depth and strong communication skills – equally effective designing a complex automation and delivering hands\-on training to a non\-technical team. Above all, this person is a proactive observer and self\-starter who identifies operational gaps and improvement opportunities independently, brings forward structured recommendations, and takes ownership of solutions from concept through delivery.
How This Role Operates
This role operates on a structured three\-part model that balances specialist autonomy with leadership alignment:
- Identify \& Propose: The specialist proactively observes how the CS team operates, identifies inefficiencies, and brings forward well\-formed recommendations. Backing up observations with proposed solutions is the standard, not the exception.
- Prioritize: The specialist maintains a living backlog of active work and proposed initiatives; the Manager determines order of priority to ensure alignment with CS and organizational goals.
- Execute: Once prioritized, the specialist owns delivery end\-to\-end, working directly with CS leaders and their teams, managing their own timeline, and operating independently or collaboratively as the work requires.
Key Responsibilities
Automation \& Agent Building
- Design, build, and deploy AI agents and workflows that automate manual, recurring processes across CS leadership and the broader Client Services team.
- Create integrations between business systems – including Salesforce, Claude, ChurnZero, Monday.com, Gong, and other CS tools – that reduce manual handoffs and surface intelligence automatically.
- Automate leadership reporting workflows, eliminating manual data pulls, uploads, and analysis cycles so CS leadership has data and insights on a defined, automated cadence.
- Maintain, iterate on, and improve automations and agents as tools, processes, and organizational needs evolve.
- Maintain a documentation library for all automations, agents, and integrations to ensure accessibility, continuity, and visibility across the organization.
Data Pipeline Ownership
- Own the data flow architecture across CS tools, defining which data goes where, how it gets there, and how it stays current and reliable.
- Ensure data integrity across all integrations, identifying and resolving data quality issues that could compromise the reliability of automated outputs or downstream decision\-making.
- Surface risk signals and customer health indicators through automated pipelines, delivering trending analysis and early\-warning intelligence to CS leadership without requiring manual data pulls.
- Partner with cross\-functional teams, including IT, Salesforce administration, RevOps, and Product Operations on pipeline design and system connectivity.
Proactive Observation, Efficiency \& Continuous Improvement
- Actively observe how the CS team and CS leadership operate, identifying process inconsistencies, manual workarounds, duplicated effort, and unmet needs that could be addressed through automation or AI.
- Maintain a running backlog of observations, proposed initiatives, and improvement opportunities; surface these to the Manager, SI Ops proactively and on a regular cadence.
- Prioritize efficiency as a core lens, identifying where varying processes, inconsistent workflows, or manual dependencies are creating friction, and driving toward standardized, scalable solutions.
- Lead vendor evaluation, tool rationalization, and experimentation, assessing new platforms and capabilities, running structured pilots, and bringing data\-driven recommendations on what to adopt, integrate, scale, or sunset.
- Establish feedback loops from training, adoption, and live workflows back into the initiative backlog, continuously refining AI priorities based on what is and is not working across the organization.
AI Adoption \& Training
- Collaborate with IT and other AI champions across the company, maintaining alignment with established guidelines, standards, and standard operating procedures.
- Build and maintain a curated prompt library tailored to Client Services use cases giving team members role\-specific prompts and workflow templates they can use immediately.
- Develop and deliver a structured AI adoption program for Client Services, training colleagues on how to use Claude, Copilot, and other AI tools effectively in their day\-to\-day work through formal training sessions, office hours, and hands\-on “build with me” labs.
- Create enablement resources – guides, videos, workflow templates, and reference documents – that support ongoing adoption and reduce dependency on live instruction over time.
- Champion AI adoption through storytelling, executive briefings, and visible wins, packaging outcomes and efficiency gains in ways that build organizational momentum and demonstrate the value of continued investment.
Business Systems Integration
- Serve as the Client Services organization’s expert on how its tools connect, identifying friction points, proposing integration solutions, and implementing them once prioritized.
- Function as a core member of the SI Ops team first, partnering closely with SI Ops colleagues on workflow design, process standardization, and operational efficiency initiatives before extending work to the broader CS organization.
- Lead vendor selection and tool rationalization within the CS tech stack, identifying redundancies, underutilized platforms, and consolidation opportunities; raising recommendations with supporting analysis.
- Act as a technical partner to non\-technical stakeholders translating business problems into automation solutions and communicating technical decisions in plain language.
- Ensure CS leadership has automated, reliable access to the data, analysis, and reports they need without manual setup, inconsistent formatting, or recurring intervention.
Governance, Documentation \& Success Metrics
- Own and maintain a practical AI governance framework for Client Services defining acceptable use parameters, data handling guidelines, and guardrails for AI tools in partnership with IT, Security, and enterprise\-wide AI governance bodies.
- Establish and enforce AI safe\-use policies and risk controls within Client Services – ensuring all automations, agents, and AI\-powered workflows meet data privacy, security, and compliance standards appropriate for a regulated financial services environment.
- Define and track success metrics for the AI and automation program – including time saved, adoption rates, agents deployed, efficiency gains, and risk signal coverage – and report outcomes to the Manager, SI Ops on a regular cadence.
- Maintain comprehensive documentation for every automation, agent, integration, and workflow, ensuring organizational continuity and enabling others to build on existing solutions.
- Surface what is working, what is not, and where to invest next, bringing forward recommendations rather than waiting for questions.
Qualifications
Required
- 3\+ years of experience in a technical role involving automation, AI tools, systems integration, or workflow development – ideally within a SaaS or financial services environment.
- Demonstrated hands\-on experience building automations and workflows using platforms such as n8n, Zapier, Make, or comparable tools.
- Experience working with AI tools including large language models (Claude, ChatGPT, or similar), including prompt engineering, agent design, and practical business application.
- Ability to work with APIs, webhooks, MCPs, and data integrations to connect business systems and automate data flows.
- Strong ability to communicate technical concepts clearly to non\-technical stakeholders – this role trains and enables people, not just builds for them.
- Demonstrated track record of proactively identifying problems and opportunities without waiting to be directed – a self\-starter who brings ideas and recommendations forward.
- Ability to manage multiple projects, prioritize independently within an agreed framework, and deliver in a fast\-paced environment.
Preferred
- Bachelor’s degree in a relevant field or equivalent professional experience.
- Experience with Salesforce, including report building, data exports, and API connectivity.
- Experience with ChurnZero or comparable customer success platforms used in enterprise SaaS environments.
- Familiarity with JavaScript, Python, or other scripting languages for custom automation logic.
- Experience designing or delivering internal training programs on AI or technology tools.
- Experience in financial services, fintech, compliance, or a regulated industry SaaS environment.
- Familiarity with AI governance concepts and responsible AI use in an enterprise context.
Tools \& Technology
- n8n (or comparable) – Required. Workflow automation and agent orchestration platform.
- Claude (Anthropic) – Required. Primary AI platform used across Client Services.
- Salesforce – Required. Core CRM from which data pipelines originate.
- REST APIs, webhooks, and MCPs – Required. For connecting systems and building reliable data pipelines.
- Prompt engineering – Required. Designing, testing, and maintaining effective prompts for LLMs.
- ChurnZero (or comparable) – Familiarity with platform structure and data model to support integration work.
- Monday.com (or comparable) – Project and workflow management; automation of task creation, routing, and status.
WE OFFER ALL FULL\-TIME TEAM MEMBERS:
- A fun, fast\-paced work environment
- Responsible PTO Plan that meets or exceeds state and local medical and family leave laws
- 11 paid holidays
- Community and social events to keep you connected and engaged
- Mental Health Benefits
- Medical, Dental and Vision insurance
- Company\-paid Group Life Insurance, Short\- and Long\-Term Disability
- Flexible Spending Account \& Health Savings Account
- Aflac Benefits – Critical Illness, Cancer Protection, \& Hospital Choice
- Pet Insurance
- 401 (k) with company match with eligibility on Day 1 of employment
- 2 Paid Volunteer Time Off Days
- And much more!
- *Part\-Time, Temporary, Contractor, and Intern positions are not eligible for company benefits, including paid time off, health insurance, and other employee benefit programs.*
AAP/EEO Statement
Ncontracts provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
Other Duties
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.
Compensation Range: $90K \- $115K
Salary Context
This $90K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Ncontracts, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($102K) sits 43% below the category median. Disclosed range: $90K to $115K.
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.
Ncontracts AI Hiring
Ncontracts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $115K - $115K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national median.
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 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).
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 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.