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About This Role
Position Summary:
The Senior Forward Deployed AI Engineer is a hands\-on engineering role responsible for rapidly delivering AI\-driven solutions that improve ARCHER’s operations across core platforms and workflows. This role works directly with business stakeholders to identify, prototype, and deploy solutions using LLMs, agentic workflows, document intelligence, and enterprise integrations. The engineer operates in a highly iterative model, using AI\-assisted development tools to deliver working solutions in days to weeks rather than traditional development cycles. The role spans Palantir Foundry/AIP, Microsoft Copilot Studio, Azure AI services, and AI\-native tools such as Claude Code and CoWork, and plays a key part in expanding ARCHER’s MCP capabilities and AI platform architecture. Success requires strong engineering fundamentals, curiosity, and the ability to independently navigate systems, data, and ambiguity to produce measurable outcomes.
Position Location: Remote
Job Responsibilities:
Key Responsibilities:
- AI Solution Execution
- Design, prototype, and deploy AI\-enabled solutions using LLMs, agents, and document workflows
- Build end\-to\-end solutions for operational use cases
- Iterate rapidly based on stakeholder feedback
- Platform \& Tooling: Work across Palantir Foundry and AIP
- Build solutions using Copilot Studio and Power Platform
- Leverage Claude Code, Claude CoWork, and skill files
- Extend MCP capabilities and reusable components
- System Integration: Integrate Archer Connect, NetSuite, Jitbit, SharePoint
- Build APIs, orchestration layers, and automation workflows
- Use Case Development: Work directly with business stakeholders to identify opportunities
- Determine build approach: off\-the\-shelf, low\-code, or custom AI
- AI\-Native Delivery: Use rapid prototyping instead of spec\-first development
- Define prompts, evaluation criteria, and guardrails
- Governance \& Scaling: Apply security, access, and audit controls
- Enable citizen builders via reusable patterns
- Hybrid Delivery: Co\-build with partners
- Transition solutions internally
KNOWLEDGE, SKILLS AND ABILITIES
Must Have:
- 5\+ years building production software and/or AI\-enabled solutions
- Experience with Palantir Foundry and/or AIP
- Experience with Copilot Studio and/or Power Platform
- Strong API and integration experience
- Experience with Claude Code, Cursor, or similar tools
- Experience with LLM workflows and agent\-based systems
- Proficiency in Python, JavaScript/TypeScript, SQL
- Ability to work directly with business stakeholders
- Strong system design and integration skills
Preferred
- Experience with MCP or similar architectures
- Experience with Azure AI services
- Experience with RAG, embeddings, evaluation frameworks
- Experience with document intelligence workflows
- Portfolio of real\-world AI applications
- WORK ENVIRONMENT
- Fast\-paced, execution\-focused environment
- Small, high\-output teams
- Rapid iteration cycles
- Direct collaboration with business stakeholders
Benefits:
- 401(K) retirement plans with matching contributions.
- Comprehensive health insurance coverage.
- Dental and vision insurance plans.
- Parental leave to support work\-life balance.
- Short\-term and long\-term disability coverage.
ABOUT ARCHER
ARCHER Systems is a leading technology\-enabled legal services company that provides pre\-settlement and post\-settlement administration services for a single event, mass tort, and class action cases with the goal of helping claimants access their settlement proceeds more efficiently and quickly. The company plans to continue leveraging technology and top\-tier talent to enhance customer service and offer new product lines and services.
ARCHER’s core offering is post\-settlement Healthcare Lien Resolution Administration and QSF (Qualified Settlement Fund) Administration and payments processing for multi\-claimant (mass tort and class action) litigation. Other services include claims administration, single event lien resolution, probate, and bankruptcy coordination, release administration, medical records review, plaintiff fact sheet, and other intake/census preparation and management. ARCHER enables law firms to focus on their litigation while ensuring that critical pre\-settlement and post\-settlement administration documents, services, business analytics, and reporting are handled efficiently and effectively.
#### Licenses \& Certifications
Preferred* Python
#### Skills
Required* APIs
Preferred* Agile Software
- Development
- SQL
- Python
#### Behaviors
Preferred* Innovative: Consistently introduces new ideas and demonstrates original thinking
- Team Player: Works well as a member of a group
#### Motivations
Preferred* Goal Completion: Inspired to perform well by the completion of tasks
- Ability to Make an Impact: Inspired to perform well by the ability to contribute to the success of a project or the organization
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Archer Systems, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Archer Systems AI Hiring
Archer Systems has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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