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About This Role
We’re building the future of legal services.
Modern Family Law (“MFL”) is seeking a highly technical, systems\-oriented, and operationally sophisticated AI Workflow Engineer / Automation Architect to help design and implement the next generation of AI\-native legal workflows and operational automation systems.
This role is responsible for building the technical “plumbing” that powers AI\-enabled legal service delivery, workflow orchestration, client automation systems, and operational integrations across the firm’s technology ecosystem.
The AI Workflow Engineer / Automation Architect will work closely with AI leadership, product teams, attorneys, developers, and operations personnel to architect scalable workflow systems that integrate AI models, internal platforms, document systems, CRMs, client portals, communication systems, and business operations.
This individual will play a critical role in helping MFL evolve into an AI\-native legal organization capable of delivering scalable, efficient, compliant, and client\-centered legal services.
The ideal candidate combines strong engineering capability with operational systems thinking and a passion for building intelligent workflow infrastructure.
Key Responsibilities
- Design, build, and maintain AI\-enabled workflow orchestration systems across legal and operational processes.
- Develop integrations between AI platforms, CRMs, document automation systems, client portals, payment systems, and internal operational tools.
- Build automation pipelines and event\-driven workflows that support legal intake, document generation, communication systems, workflow routing, and escalation protocols.
- Collaborate with Legal Knowledge Engineers and Product Managers to translate legal workflows into scalable technical architectures.
- Implement API integrations across third\-party platforms and internal systems.
- Develop retrieval, prompt orchestration, and workflow execution systems supporting AI agents and AI\-assisted processes.
- Assist in designing scalable architectures for AI\-powered client experiences and internal operational systems.
- Monitor workflow reliability, system performance, automation effectiveness, and operational scalability.
- Troubleshoot automation failures, workflow bottlenecks, and integration issues.
- Participate in security, governance, auditability, and compliance planning related to AI workflows.
- Document workflow architectures, system logic, integration maps, and operational standards.
- Evaluate emerging automation technologies, orchestration frameworks, and AI infrastructure tools.
Requirements Required Qualifications
- Bachelor’s degree or equivalent practical experience in software engineering, computer science, information systems, automation engineering, or related field.
- Deep experience in workflow automation, systems integration, software engineering, AI implementation, or automation architecture.
- Production\-grade Python and using sound software engineering practices, including testing, code reviews, version control, and CI/CD.
- Design, build, and optimize AI agent workflows using orchestration frameworks (e.g., LangGraph, LangChain), vector retrieval, LLM Observability platforms (e.g., LangVuse, LangSmith), and API/webhook integrations.
- Strong understanding of APIs, workflow orchestration, event\-driven systems, and systems integration.
- Experience working with automation platforms, orchestration tools, or AI infrastructure frameworks.
- Production\-grade Python plus solid engineering practices (testing, code review, version control), and agent orchestration frameworks (LangGraph/LangChain, vector retrieval, LLM observability, e.g., LangFuse/LangSmith, API/webhook integrations).
- Strong analytical, troubleshooting, and systems\-thinking abilities.
- Ability to work cross\-functionally with technical and non\-technical stakeholders.
- Excellent organizational and communication skills.
- Ability to manage multiple complex projects in fast\-moving environments.
Preferred Qualifications
- Experience with generative AI systems and AI orchestration frameworks.
- Familiarity with OpenAI, Anthropic, LangChain, LlamaIndex, vector databases, retrieval systems, or workflow automation platforms.
- Experience integrating CRM systems, document automation tools, client portals, or SaaS platforms.
- Experience within legal technology, healthcare technology, financial services, or other regulated industries.
- Familiarity with cloud infrastructure and scalable automation architectures.
- Understanding of AI governance, auditability, data security, and confidentiality considerations.
- Experience supporting AI\-powered conversational systems or agent architectures preferred.
Characteristics of the Ideal Candidate
The ideal candidate is a systems builder who enjoys solving complex operational problems through thoughtful engineering and intelligent automation.
They are highly organized, technically curious, operationally minded, and energized by the opportunity to help redesign legal service delivery through scalable AI infrastructure and workflow systems.
This individual should possess the ability to:
- Think architecturally and systematically.
- Translate operational needs into scalable technical solutions.
- Collaborate effectively across legal, operational, and technical teams.
- Identify inefficiencies and design elegant workflow solutions.
- Balance innovation, reliability, security, and operational scalability.
Compensation \& Benefits
Modern Family Law offers highly competitive compensation, performance incentives, comprehensive benefits, and the opportunity to help shape one of the nation’s most innovative family law organizations.
Compensation will be commensurate with technical capability, engineering sophistication, operational impact, and strategic contribution.
About Modern Family Law
Modern Family Law is one of the nation’s leading family law firms, focused on combining exceptional legal representation with innovation, technology, and client\-centered service.
The firm is actively exploring AI\-native legal workflows, advanced client experience systems, and new models for increasing accessibility, efficiency, and scalability within legal services.
We believe the future of law belongs to organizations capable of combining outstanding legal talent with thoughtful technological innovation.
Skills and Competencies:
- Ability to communicate professionally and interact effectively with employees, managers, leadership, attorneys, support staff, and external partners
- Ability to be a proactive self\-starter who can operate independently while managing multiple priorities and deadlines
- Demonstrated attention to detail, reliability, organization, and follow\-through
- Flexibility and ability to respond positively to shifting priorities and business needs
- Ability to work under pressure and manage competing priorities while maintaining accuracy and professionalism
- Ability to work in a primarily computer\-based environment, including extended periods of sitting, screen time, typing, and use of standard office equipment
- Ability to occasionally lift and move office equipment or materials weighing up to 10 pounds
- Ability to work occasional evenings or adjusted hours as needed to support onboarding, offboarding, or time\-sensitive operational needs
ADA Compliance: All candidates and incumbents are expected to perform the duties as assigned so long as they can meet the expectations set forth with or without reasonable accommodations. Should a candidate or incumbent require accommodation, they need to advise the Director of People \& Culture in advance.
Benefits
- Health Care Plan (Medical, Dental \& Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary \& AD\&D)
- Paid Time Off (Vacation, Sick \& Public Holidays)
- Short Term \& Long Term Disability
- Training \& Development
Work From Anywhere * \- eligible after 6 monthshttps://modern\-family\-law\-1\.workable.com/backend/jobs/5910536/app\-form
Salary Context
This $120K-$135K 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 Modern Family Law, 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 ($127K) sits 41% below the category median. Disclosed range: $120K to $135K.
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.
Modern Family Law AI Hiring
Modern Family Law has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in VA, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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