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About This Role
Mindex is looking for an AI Solutions Design Manager to lead the strategy, design, and delivery of AI and Generative AI solutions that solve real business challenges and improve operational performance. This role blends hands\-on technical expertise with strategic leadership — you'll guide cross\-functional teams to identify high\-value opportunities, architect scalable AI/GenAI systems, and drive successful adoption across the organization.
You'll be equally comfortable setting direction at the roadmap level and getting into the weeds on solution architecture, model selection, and RAG/agent design — while also serving as a trusted advisor who can translate complex AI concepts into language business leaders can act on.
Compensation
$160,000 \- $180,000, plus a 20% bonus based on company performance goals.
What You'll Accomplish
*Strategy \& Innovation*
- Develop and maintain an AI/GenAI vision and roadmap aligned to business goals, identifying high\-value use cases across business units.
- Evaluate emerging AI technologies — LLMs, copilots, multimodal models, autonomous agents, automation platforms — and recommend practical, prioritized applications.
- Establish standards and reusable design patterns for prompt engineering, model tuning, and GenAI solution architecture.
*Solution Architecture \& Delivery*
- Lead end\-to\-end design of AI/ML and GenAI solutions from concept to production, translating business requirements into technical specifications, data flows, and architectures.
- Architect GenAI\-powered applications such as chatbots, copilots, summarization engines, and content\-generation workflows.
- Oversee development of reusable GenAI components — prompt libraries, embeddings, vector search, knowledge models, and RAG systems — and guide model selection and fine\-tuning decisions.
- Partner with data engineering and IT teams to ensure solutions are scalable, maintainable, and meet enterprise standards for security, privacy, and governance.
*Project \& Team Leadership*
- Manage the AI/GenAI project portfolio, including scoping, planning, resourcing, and execution.
- Lead cross\-functional teams of data scientists, engineers, analysts, and business stakeholders through agile delivery cycles.
- Serve as a trusted advisor to business leaders on AI capabilities, risks, and best practices, and support change management for organization\-wide adoption.
*Governance \& Responsible AI*
- Ensure AI/GenAI solutions meet ethical, fairness, privacy, and regulatory standards, including content filtering, hallucination mitigation, and usage guardrails.
- Define acceptable\-use policies and risk frameworks for internal GenAI tools, and maintain documentation, monitoring, and lifecycle management processes.
- Oversee adoption and governance of enterprise GenAI platforms (Microsoft 365 Copilot, Azure OpenAI, GitHub Copilot, etc.) in partnership with security and infrastructure teams.
*Enablement \& Operationalization*
- Develop training programs on responsible AI/GenAI usage and coach teams on effective prompt engineering and human\-in\-the\-loop workflows.
- Define KPIs to measure GenAI performance and business value, and monitor for model drift, usage patterns, hallucination rates, and user satisfaction.
- Lead continuous improvement cycles for AI/GenAI systems based on data\-driven insights.
Requirements
- Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or a related field.
- 7\+ years of experience in AI/ML, data analytics, or software engineering, including 3\+ years leading technical teams or designing enterprise AI solutions.
- Strong understanding of machine learning, LLMs, NLP, deep learning, and automation technologies.
- Hands\-on experience with modern AI platforms (Azure ML, Databricks, OpenAI, AWS, etc.).
- Proven ability to translate business needs into scalable technical solutions.
- Strong leadership, communication, and stakeholder engagement skills.
Preferred
- Experience deploying enterprise GenAI or LLM\-based applications.
- Cloud certifications (Azure, AWS, GCP).
- Experience with MLOps, RAG, vector databases, and prompt engineering.
- Previous leadership experience across hybrid technical/business teams.
Success Indicators
- Delivery of AI/GenAI solutions with measurable business impact.
- Strong adoption and satisfaction across business teams.
- Advancement of organizational AI maturity and literacy.
- Demonstrated innovation and ROI from AI\-enabled transformation.
Benefits
- Hybrid
- Medical, Dental, and Vision Insurance
- 401(k) with Company Match
- Company\-Paid Life and Disability Insurance
- Professional Development Opportunities
Additional Information
- Applicants must be authorized to work for any employer in the United States. Mindex is unable to sponsor employment visas at this time.
Physical Requirements/Conditions
- Prolonged periods sitting at a desk and working on a computer.
- No heavy lifting is expected. Exertion of up to 10 lbs.
Salary Context
This $160K-$180K range is below the median 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 Mindex, 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 ($170K) sits 21% below the category median. Disclosed range: $160K to $180K.
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.
Mindex AI Hiring
Mindex has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochester, NY, US. Compensation range: $180K - $180K.
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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