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About This Role
Job Description
Ready to Help Shape the Future of Legal Tech?!
At Litera, we don’t just build software, we transform how the world’s top law firms operate. Every day, we Raise The Bar™️ for what’s possible through AI, innovation, and solutions that power millions of legal professionals worldwide. If you’re energized by scale, real impact, and meaningful challenges, you’ll feel right at home here.
Where You’ll Work
This is a hybrid role based in Denver, CO with the expectations to be in office at least 3 days a week for collaboration and connection.
Why this Role Matters
At Litera, AI is becoming a critical enabler of how we build products, improve customer experiences, and drive innovation. As an LLM Ops Engineer, you will create the secure, scalable, and reliable foundation that allows our engineering teams to leverage AI confidently and efficiently across the business. Your work will ensure that AI capabilities are available, governed, cost\-effective, and ready to support production applications at scale. This role is instrumental in accelerating AI adoption while maintaining the performance, security, and resilience required for enterprise software.
What You’ll Deliver
- Build and operate a scalable AI platform that enables engineering teams to seamlessly access and deploy models across multiple providers and environments.
- Ensure high availability and resiliency of AI services through intelligent routing, failover strategies, and production\-grade infrastructure.
- Establish secure and compliant AI operations by protecting model access, safeguarding sensitive data, and enforcing governance standards.
- Create a consistent developer experience through unified APIs, self\-service capabilities, tooling, and best practices that accelerate AI adoption.
- Optimize AI platform performance, reliability, and cost efficiency through proactive monitoring, analytics, and provider strategy management.
- Lead the evolution of Litera’s AI operations capabilities by evaluating emerging technologies and recommending scalable solutions.
- Deliver observability and operational excellence through dashboards, alerting, quality monitoring, and service\-level metrics.
- Support the safe deployment of AI solutions by implementing testing frameworks, quality controls, and production readiness standards.
We’re committed to creating an inclusive environment. If you need accommodations at any point in the process or in the role, we’re here to support you.
What You’ll Bring
Must\-Haves:
- 3\+ years of experience in DevOps, Platform Engineering, MLOps, or a related field, including hands\-on experience operating LLMs in production environments.
- Experience deploying, managing, and scaling models across multiple AI providers such as OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or Google Vertex AI.
- Strong expertise in building highly available, secure infrastructure, including load balancing, failover strategies, secrets management, and access controls.
- Experience with API management, gateway technologies, and production\-grade AI service operations.
- Strong Python programming skills with experience developing and supporting scalable systems.
- Proven ability to solve complex technical challenges and thrive in a fast\-paced, evolving environment while collaborating across teams.
Nice to Haves:
- Experience fine\-tuning or training large language models for domain\-specific applications.
- Familiarity with ML orchestration tools and frameworks such as Kubeflow, MLflow, or Apache Airflow.
- Experience with LLM evaluation frameworks, retrieval\-augmented generation (RAG), vector databases, or inference optimization techniques.
- Knowledge of infrastructure\-as\-code, Kubernetes, compliance frameworks, or large\-scale AI cost optimization strategies.
We know great candidates don’t always check every box. If you’re excited about this role, we encourage you to apply.
What You’ll Experience
- A team that shows up. Work alongside people who collaborate, support one another, and lead with integrity.
- Global Reach. Partner with teams around the world to solve complex challenges that matter.
- Real opportunity for growth. Expand your impact through meaningful stretch opportunities, visibility and career development.
- AI\-driven innovation. Work at the intersection of legal technology, customer outcomes, and cutting\-edge AI.
Pay Transparency for Colorado Applicants
The base salary range for this role is $105,000 to $130,000 USD. Final compensation will be determined based on experience, skills, education, and other relevant qualifications. This role is also eligible to participate in a company bonus plan. In addition to base salary, Litera offers a comprehensive benefits package, including medical, dental, and vision coverage, a 401(k) with company match, and incentive and recognition programs. Benefits are subject to eligibility requirements.
\#LI\-Hybrid
Litera is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Salary Context
This $105K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Litera, 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. This role's midpoint ($117K) sits 45% below the category median. Disclosed range: $105K to $130K.
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.
Litera AI Hiring
Litera has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $130K - $130K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below 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 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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