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About This Role
About Us
At Litify, we're revolutionizing the Legal industry by being the platform powering legal's top performers. As a trailblazer in legal technology, Litify delivers an all\-in\-one legal operating solution that empowers law firms and legal departments to achieve consistent success by continually standardizing, measuring, and improving their legal operations.
Our mission is clear: to deliver better business outcomes to our clients, so they can focus on delivering the best legal service and outcomes to their clients. 400\+ enterprise businesses and 55K\+ legal professionals trust Litify to amplify their impact with innovative technology and service that stands the test of time.
Backed by Bessemer Venture Partners, Litify is proud to be recognized as one of Inc. 5000 and Deloitte Technology Fast 500's fastest\-growing private companies in America along with numerous awards for our unparalleled software. With offices in the vibrant cities of New York and New Orleans, we're at the heart of legal innovation.
About the Role
We are seeking a Solution Architect to join the Litify AI team within our Professional Services organization. Operating with a high degree of autonomy, you will lead the practical application of advanced AI technologies within the legal sector, helping to define and establish new industry standards.
You will direct the end\-to\-end implementation of Litify AI for our clients, which includes designing custom agent workflows on the Salesforce platform, providing strategic guidance to clients, and executing successful deployments. While collaborating closely with a Project Manager, you will maintain complete ownership of the technical solution, including the system architecture, configuration, and final project outcomes.
You will:
- Demo Litify AI capabilities to clients and prospects, tailoring demonstrations to their specific legal workflows and use cases.
- Lead discovery sessions to solicit and document client requirements, translating them into detailed solution designs.
- Build and deploy custom AI agent workflows on Litify AI's Salesforce\-based framework, owning configuration from design through go\-live.
- Serve as the primary technical advisor to clients on Litify AI capabilities, best practices, and product roadmap.
- Work closely with your Project Manager to scope, plan, and deliver AI implementations on time and within budget.
- Partner across Sales, Engineering, and Customer Success to align on client needs, surface feedback, and drive successful outcomes across the full client journey.
- Define success metrics for each deployment and drive adoption to ensure clients realize measurable value.
- Document solution designs, configuration decisions, and implementation guides for client and internal use.
- Collaborate with Litify's product and engineering teams to surface client feedback and influence the AI roadmap.
You have (required):
- 4\+ years of Salesforce implementation experience, with advanced mastery of Salesforce Flows and modern automation tooling.
- You're a trusted advisor to clients — not just a technical resource. You're equally at home walking a C\-suite executive through a complex AI architecture and translating that same concept for a non\-technical end user.
- Proven capability to own a technical workstream end\-to\-end — from requirements to delivery — with minimal oversight.
- Experience in a professional services, consulting, or client\-facing implementation role.
- Experience designing and deploying AI agents, or automated workflow solutions in a client\-facing role.
- Hands\-on experience with LLM\-based tools, prompt engineering, and agent orchestration frameworks.
- Strong understanding of the Salesforce data model, security architecture, object relationships, and platform capabilities.
You have (nice\-to\-have):
- Experience with Litify or other legal operations platforms.
- Familiarity with law firm workflows: intake, matter management, billing, and document generation.
- Salesforce Certifications: AI Specialist, Administrator, Platform App Builder, or Consultant.
Disclosure:
The estimated base salary pay range for this role is $100,000\-$120,000\. You may also be offered a bonus and benefits.
Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide.
Individual pay rate decisions are based on a number of factors, including qualifications for the role, experience level, skill set, and balancing internal equity relative to peers at the company.
The range above is for the expectations as laid out in the job description, however we are often open to a wide variety of profiles, and recognize that the person we hire may be less experienced (or more senior) than this job description as posted. If that ends up being the case, the updated salary range will be communicated to you as a candidate.
Ready to make a difference with us? Discover more about Litify and explore our open roles at www.litify.com. Connect with us on Instagram (@LitifyHQ), Twitter (@LitifyHQ), or LinkedIn.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Litify, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($110K) sits 50% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Litify AI Hiring
Litify has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $120K - $120K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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