Interested in this AI/ML Engineer role at H.W. Kaufman Group?
Apply Now →Skills & Technologies
About This Role
:
Responsibilities:
H.W. Kaufman Group is building an AI Value Engineering capability inside Information Technology to engineer measurable business value across the enterprise. The AI Value Engineering team operates at the intersection of application development, RPA, data, business operations, and AI\-enabled delivery. Its charter is to deliver high\-impact solutions across quick\-strike and deep\-dive engagements, partner closely with business stakeholders, transition proven solutions to sustaining teams, and stop initiatives quickly when they are not proving value. The team is measured on portfolio impact, successful handoffs, quantified business value, disciplined focus, and the ability to avoid becoming a maintenance or production\-support function.
This role is for a hands\-on, AI\-enabled developer who can move across applications, automation, data, and business operations to identify high\-impact opportunities, build working solutions, prove their work, and transition responsibly to sustaining teams.
We are looking for developers for whom building software is more than a job. Strong candidates are resourceful, curious, disciplined, and energized by solving problems. They have opinions shaped by experience, reading, experimentation, and exposure to good engineering practices. They understand that AI changes the speed of software delivery, but not the need for judgment, craftsmanship, business understanding, and accountability.
This role is best suited for a T\-shaped or comb\-shaped technologist: broad enough to wire together platforms, data, APIs, automations, and user workflows; deep enough to make sound technical decisions and avoid fragile solutions. What Success Looks Like* Take a loosely defined business problem, clarify the value hypothesis, and produce a working demonstration quickly
- Know when an AI agent’s recommendation is plausible, risky, over\-engineered, or misaligned with enterprise supportability
- Move comfortably between front\-end, back\-end, integration, data, workflow automation, and platform configuration
- Actively look for simpler, faster, more supportable paths rather than merely implementing the first technical answer
- Produce solutions that can be handed off cleanly; you do not create hidden dependencies on yourself
- Communicate tradeoffs clearly to technical and non\-technical audiences
Responsibilities* Deliver quick\-strike solutions in days or weeks where business value is clear and the path is known
- Contribute to deeper exploratory engagements over one to three months where the solution is uncertain, but the potential value is high
- Build prototypes, automations, integrations, data\-driven workflows, and application features across Salesforce, MuleSoft, .NET, RPA, AI/ML tooling, and related enterprise platforms
- Use AI development tools such as Claude, Codex, Gemini, Copilot, or similar agents to accelerate delivery while validating architectural fit, maintainability, security, and operational risk
- Work directly with underwriting, claims, operations, and IT stakeholders to understand workflows, pain points, value drivers, and practical adoption constraints
- Translate business problems into working software, measurable experiments, and clear handoff packages
- Document architecture, configuration, dependencies, known limitations, prompts, runbooks, and operational considerations for the receiving team
- Recommend pivoting or stopping work when an initiative is not proving valuable, without treating that as failure
- Shadow and learn business workflows so solutions are grounded in how work gets done
Qualifications:
- Professional software development experience in enterprise environments with 5\+ years of experience
- Full\-stack capability across application development, integrations, APIs, data access, workflow automation, and user\-facing delivery
- Experience with at least several of the following: Salesforce, MuleSoft, .NET/C\#, JavaScript/TypeScript, SQL, REST APIs, RPA/workflow tools, cloud services, CI/CD, or enterprise data platforms
- Practical experience using AI\-assisted development tools or a demonstrated ability to adopt them quickly and responsibly
- Strong debugging, problem decomposition, and self\-directed learning skills
- Ability to evaluate code quality, maintainability, patterns, security implications, and operational fit
- Ability to write clear technical documentation and conduct knowledge transfer with receiving teams
- Comfort working in timeboxed engagements with defined outcomes, checkpoints, and handoff expectations
- Experience in insurance, underwriting, brokerage, claims, or other workflow\-heavy business domains is a plus
- Exposure to enterprise architecture patterns, design patterns, domain modeling, relational database design, integration patterns, or similar engineering disciplines
- Experience building automations or AI\-enabled solutions that combine applications, data, documents, and human review workflows
- Experience with prompt engineering, LLM evaluation, retrieval\-augmented generation, agentic development workflows, or AI governance practices
- Ability to operate inside appropriate enterprise guardrails while still challenging assumptions and finding better approaches
Complementary Strengths
Because this team is intentionally built from generalists who can go deep, we value candidates who bring complementary strengths that broaden the team’s overall capability. Experience in any of the following areas is a plus, but not required:* Information security — secure development practices, identity and access management, data protection, secrets management, vulnerability remediation, secure API design, or practical experience partnering with security teams
- Azure and cloud services — Azure App Services, Azure Functions, Logic Apps, Azure SQL, Storage Accounts, Key Vault, Service Bus, Entra ID, Azure DevOps, or similar cloud\-native services
- Infrastructure as Code / DevOps — Terraform, Bicep, ARM templates, CI/CD pipelines, environment configuration, deployment automation, observability, or release governance
- Enterprise integration — API gateways, MuleSoft, event\-driven integration, message queues, service orchestration, or integration monitoring
- AI governance and operationalization — prompt/version management, LLM evaluation, data leakage prevention, human\-in\-the\-loop review, auditability, monitoring, or safe deployment of AI\-assisted workflows
\#LI\-CC1
About Our Company:
About Our Company
H.W. Kaufman Group is a powerful global network of companies dedicated to shaping the future of insurance. With thousands of dedicated professionals across an extensive network of over 60 offices around the world, we lead by offering innovative solutions that are at the forefront of the industry. We are privately owned and thus free from the influence of Wall Street. This allows us the ability to adapt to constantly fluctuating market conditions. From brokerage, underwriting, and real estate to claims, loss control and risk management services, our depth of services is unrivaled. Equal Opportunity Employer
The H.W. Kaufman Group of companies is an equal opportunity employer. All employment decisions are based on business needs, job requirements and individual qualifications, without regard to race, color, religion, gender, gender identity, age, national origin, disability, veteran status, marital status, pregnancy, sexual orientation, genetic information or any other status or condition protected by the laws or regulations in the locations where we operate.
In addition, Kaufman will make reasonable accommodations to known physical or mental limitations of an otherwise qualified person with a disability, unless the accommodation would impose an undue hardship on the operation of our business.
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 H.W. Kaufman Group, 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.
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.
H.W. Kaufman Group AI Hiring
H.W. Kaufman Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Farmington Hills, MI, US.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.