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About This Role
Agentic Engineer \| 100% Remote (WFH) Opportunity
General Summary
The Agentic Engineer will be responsible for designing, building, and deploying production\-grade agentic AI systems that autonomously execute multi\-step workflows across our insurance operations. You will work at the intersection of LLM orchestration, tool integration, and enterprise systems to bring agent\-native capabilities to life. Responsibilities span the full agentic stack: from defining agent anatomy (triggers, loops, tools, memory, human gates, and observability) to hardening agents for reliability, safety, and governance in a regulated environment.
Essential Duties and Responsibilities
- Design and build end\-to\-end agentic workflows that autonomously execute multi\-step business processes, including FNOL processing, claims triage, and audit automation.
- Develop and maintain agent orchestration patterns using frameworks such as LangChain, LangGraph, or custom\-built agent loops, integrating LLMs with enterprise tools, APIs, and data platforms.
- Implement robust tool use layers, including function calling, MCP integrations, and structured output validation, to connect agents to internal systems and external services.
- Design and enforce human\-in\-the\-loop gate patterns, escalation logic, and approval checkpoints to ensure agents operate within defined authority boundaries in a regulated insurance environment.
- Build and manage memory tier strategies for agents, including in\-context, external retrieval (RAG/vector stores), and episodic memory patterns appropriate to each workflow.
- Instrument agents with full observability: structured logging of agent reasoning traces, tool call outcomes, token usage, latency, and failure modes to support monitoring, debugging, and audit requirements.
- Implement prompt injection defenses, output validation, and agent identity controls to maintain security and prevent misuse or unintended agent behavior in production.
- Evaluate and benchmark agent performance through end\-to\-end testing, red\-teaming, and simulation of edge cases to validate reliability before production deployment.
- Partner with Product Owners and Enterprise Architects to translate business workflows into agent\-native designs, ensuring alignment on scope, authority, and fallback behaviors.
- Champion CI/CD best practices for agent deployments, including versioned prompts, tool schemas, and agent configurations with rollback capability across Dev, QA, and Prod environments.
- Contribute to and extend internal agentic infrastructure, including shared tool registries, reusable agent templates, and platform abstractions that accelerate agent development across the team.
- Other duties as assigned.
Requirements \| Background and Experience/Expertise
- Previous experience building and deploying AI agents, LLM\-powered applications, or agentic workflow systems in a production\-like environment is required.
- Demonstrated experience with agentic orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, or custom agent loops) and LLM tool/function calling patterns.
- Strong understanding of machine learning techniques, software architecture and distributed systems, including how agents interact with APIs, databases, message queues, and external services at runtime.
- Hands\-on experience with RAG architectures, vector databases (e.g., Pinecone, pgvector, Chroma), and embedding strategies for grounding agent responses in enterprise knowledge.
- Familiarity with AI safety and trust concepts relevant to agentic systems: prompt injection, output sanitization, privilege minimization, and human oversight patterns.
- Ability to write robust, production\-quality code in Python; proficiency with async patterns and REST APIs for building reliable agent tool integrations.
- Experience deploying and serving open\-source LLMs using frameworks such as vLLM, llama.cpp, TensorRT\-LLM, or similar inference engines
- Familiarity with model optimization techniques (quantization, batching strategies, GPU memory management) for production workloads
- Experience deploying containerized AI services using Docker and Kubernetes (or OpenShift/EKS) within cloud environments (AWS/Azure), including managing inference infrastructure and API gateways.
- Exemplary written, verbal, listening, and interpersonal communication skills.
- Excellent analytical, problem\-solving, and decision\-making skills.
- Ability to work both independently and collaboratively as part of a team.
Work Environment:
- Remote: This role is a remote (work from home (WFH)) opportunity, and only open to candidates currently located in the United States and able to work without sponsorship.
- It requires a suitable space that provides a private and quiet workplace.
- Expected Work Hours: Schedules are set to accommodate the requirements of the position and the needs of the organization and may be adjusted as needed.
- Travel: May be required to travel to off\-site location(s) to attend meetings, as necessary
Salary Range: $105,000 \- $160,000 and a comprehensive benefits package, please follow the link to our benefits page for details! EMPLOYERS Benefits and Perks
About EMPLOYERS
As a dynamic, fast\-growing provider of workers' compensation insurance and services, we are seeking a goal\-oriented individual willing to put their ideas to work!
We offer a positive, challenging work environment, combined with an opportunity to build your career as you help us grow our business, in innovative and imaginative ways that are uniquely EMPLOYERS!
Headquartered in Nevada, EMPLOYERS attributes its long\-standing success to its most valuable resource, our employees across the United States. EMPLOYERS is known for the quality service and expertise we provide to our clients, and the exemplary work environment we provide for our employees.
We live and breathe our core values: Integrity, Customer Focus, Collaboration, Initiative, Accountability, Innovation, and Personal Fulfillment. These are the pillars that support how we do business with our clients as well as how we treat each other!
At EMPLOYERS, you’ll discover an energetic environment that inspires top achievement. As “America’s small business insurance specialist”, we have the resources, a solid reputation and an expanding nationwide identity to enrich your work/life and enhance your career. \#LI\-Remote
Salary Context
This $105K-$160K 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 employers, 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 ($132K) sits 38% below the category median. Disclosed range: $105K to $160K.
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.
employers AI Hiring
employers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $160K - $160K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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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