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About This Role
At InsCipher, our commitment to our customers is what drives us.
Ours is a culture of innovation and progress. We are a creative team of doers constantly striving to develop value\-driven products and services for our customers.
Our ultimate goal is to become the trusted authority and leading partner for state departments of insurance, surplus lines associations, and brokers nationwide. We’re achieving that goal by enhancing every facet of our customers reporting and tax filings through education and innovative, streamlined compliance solutions.
We’re growing fast and want you to be a part of it!
We are seeking a Lead AI Engineer to serve as our organization's foremost technical authority on artificial intelligence strategy, architecture, and adoption. At Veracity, that means shaping how AI is built and scaled across a customer\-facing digital insurance platform serving small business owners nationwide – from how we surface coverage recommendations to how we prepare for a world where AI agents increasingly research and purchase insurance on behalf of human customers.
This is not a people management role – it is a systems leadership role. You will act as the connective tissue between engineering, product, and business leadership, making and owning the high\-stakes technical decisions: build vs. buy, platform selection, architectural patterns, and AI tooling standards. You will bring structure and discipline to our AI practice while operating with the urgency and adaptability of a fast\-moving, independent company.
Key Responsibilities
Technical Strategy \& Direction
- Define the AI engineering roadmap and architecture standards across the organization
- Lead build vs. buy vs. integrate decision\-making for AI systems and platforms – and be accountable for articulating how you arrived at those decisions
- Evaluate emerging tools, frameworks, and models; provide clear, defensible recommendations to leadership
- Serve as the ultimate technical escalation point for complex AI/ML system design challenges
Enterprise Systems \& Scaling
- Architect AI solutions for our enterprise platform – which has already been substantially rebuilt and requires thoughtful evolution, not ground\-up construction
- Design systems for scale, reliability, and cost\-efficiency in production environments
- Establish LLMOps practices – evaluation and regression suites for LLM\-powered features, cost and quality observability, versioned prompts/configs with staged rollout and rollback, and production guardrails
- Ensure AI systems integrate cleanly with existing product and engineering infrastructure
Cross\-Functional Collaboration
- Partner closely with Product and Engineering leadership to align AI capabilities with business outcomes
- Receive operational support to help initiate and coordinate cross\-functional engagement, allowing you to stay focused on technical leadership and decision\-making
- Provide clear guidance on resource allocation – who owns what, who should be engaged, and in what sequence
- Translate complex technical concepts for non\-technical audiences, including executives and business stakeholders
- Challenge direction constructively and push back on approaches that compromise long\-term system health
AI Coding \& Process Transformation
- Champion and introduce AI\-assisted coding practices and developer tooling across the engineering organization
- Define standards for responsible AI development including guardrails, evaluation frameworks, and security considerations
- Drive continuous improvement in how the organization builds and ships AI\-powered features
What This Role Is Not
This is not a people manager role. You will not carry direct reports or be evaluated on team headcount. You may provide technical direction to contributors, but managing people is the exception, not the expectation.
Requirements and Qualifications
Required
- Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field (or equivalent practical experience)
- 8\+ years of software engineering experience, with demonstrated mastery designing and shipping production systems where correctness, reliability, and auditability matter
- 2\+ years building production LLM/GenAI and agentic systems, plus fluency with AI\-assisted coding tooling and the judgment to set org\-wide standards, evals, and guardrails for AI\-generated code
- Sound judgment about where AI belongs and where it must not – comfortable with probabilistic agents for customer\-facing and research tasks, while keeping policy binding, money movement, and compliance flows deterministic, auditable, and human\-governed
- Experience making and communicating build vs. buy vs. integrate decisions at an organizational level
- Proficiency in Python and the modern GenAI application stack – agent/orchestration frameworks (e.g. LangChain, LlamaIndex, or equivalents), model\-provider SDKs, vector databases, and evaluation tooling
- Exceptional written and verbal communication skills – you can write a crisp architecture decision record and present it to a board\-level audience
Preferred
- Experience in a technical lead or principal engineer role with broad organizational influence
- Background working closely with product engineering teams in a dual\-track agile model
- Familiarity with RAG architectures, vector databases, and semantic search at scale
- Experience with AI cost management, observability, and governance frameworks
Perks
- Health, dental, and vision plans
- Amazing work\-life balance with 4 weeks of Paid Time Off
- 10 Paid Company Holidays with 2 floating holidays
- 401K Programs with employer match
- Personal assistance programs for support in a healthy personal and work life
Why InsCipher?
At InsCipher, you'll join a team of disruptors, innovators, and forward\-thinkers. We're not just changing the game; we're creating a new one. We offer a dynamic, inclusive work environment where your ideas are valued, and your contributions lead to real change. With us, you'll have the opportunity to:
- Work on cutting\-edge projects that are reshaping an industry
- Collaborate with a team of passionate, like\-minded professionals
- Enjoy a culture that values flexibility, innovation, and personal growth
Compensation Range: $170k/yr \- $215k/yr
We are proud to be an equal\-opportunity employer. We are committed to providing equal opportunities to all qualified applicants, regardless of race, color, religion, sex, national origin, disability, or any other legally protected characteristics.
If you need accommodation, please let us know during the interview process.
Salary Context
This $170K-$215K range is above the median 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 InsCipher, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $170K to $215K.
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.
InsCipher AI Hiring
InsCipher has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $215K - $215K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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