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About This Role
Job Description
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At EisnerAmper, we look for individuals who welcome new ideas, encourage innovation, and are eager to make an impact. Whether you’re starting out in your career or taking your next step as a seasoned professional, the EisnerAmper experience is one\-of\-a\-kind. You can design a career you’ll love from top to bottom – we give you the tools you need to succeed and the autonomy to reach your goals.
EisnerAmper is seeking an AI Platform Architect, Strategy \& Enablement who will serve as the technical backbone of EisnerAmper’s AI transformation, owning the performance, reliability, and cost\-efficiency of the EisnerAI Platform. This role will own and execute AI Platform strategy, defining the multi\-year roadmap, cross\-service\-line capability, and coherence that position the platform as a strategic asset, scaling to 1,000 deployed AI agents across every solution, from intelligent tax preparation to agentic audit workflows. The role will build model lifecycle, evaluation, and multi\-vendor orchestration as usage expands across every team.
Reporting to the Chief AI Officer, this is a high\-impact, high\-visibility role at the intersection of platform architecture, AI engineering, and strategic operations. The critical dimensions of this position are internal \- elevating platform fluency and technical depth across teams and external — cultivating strategic partnerships across the hyperscaler and AI vendor ecosystem to extend platform capability into wide ecosystem integration opportunities.
What it Means to Work for EisnerAmper:
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- You will get to be part of one of the largest and fastest growing accounting and advisory firms in the industry
- You will have the flexibility to manage your days in support of our commitment to work/life balance
- You will join a culture that has received multiple top “Places to Work” awards
+ We believe that great work is accomplished when cultures, ideas and experiences come together to create innovative solutions
+ We understand that embracing our differences is what unites us as a team and strengthens our foundation
+ Showing up authentically is how we, both as professionals and a Firm, find inspiration to do our best work
What Work You Will be Responsible For:
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Own and Execute AI Platform Strategy
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- Own the multi\-year platform roadmap — architecture, model lifecycle, and governance\-as\-code — scaling the EisnerAI Platform on Azure toward 1,000\+ deployed agents while ensuring Tax, Audit, Advisory, and Outsourcing build on one coherent foundation.
- Define and enforce model lifecycle standards (active, legacy, deprecated, retired) and evaluation benchmarks across the firm's multi\-LLM environment, turning vendor and model choices — GPT\-4o, o3, and what comes next, into a deliberate portfolio strategy rather than ad hoc adoption.
- Own the connection between the EisnerAI Platform and the firm's broader technology ecosystem (ETI), establishing this bridge proactively rather than retrofitting it once scale makes it expensive.
- Ensure the platform roadmap and standards account for both B2B and B2C dimensions of the firm's AI use — firmwide client service delivery as well as direct\-to\-client and wealth management contexts.
Lead Platform Strategy and Architecture
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- Architect and evolve the platform's core data, application, and agentic layers on Azure AI Foundry, establishing the reference architecture and technical standards that govern how every solution from intelligent tax preparation to agentic audit workflows gets built.
- Codify AI and data governance (NIST AI RMF, PCAOB, Section 7216, and emerging requirements including the EU AI Act) into enforceable platform\-level controls and automated compliance checks, in partnership with the firm's existing AI governance function.
- Lead AI performance engineering across the firm's LLM ecosystem, benchmarking models for accuracy, latency, cost, and quality, and directing routing and optimization strategy to continuously lower cost\-per\-agent without sacrificing output quality.
Build and Operate Cross\-Cutting Capability Enablement
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- Build and operate the platform's real\-time performance monitoring layer, hallucination rates, retrieval precision, adoption, and cost\-per\-interaction, as a shared capability every service line draws on rather than one each team rebuilds independently.
- Own identity, access, and security controls for AI systems and agents firmwide, establishing the guardrails that let Tax, Audit, Advisory, and Outsourcing scale their own use cases safely on shared infrastructure.
- Deliver executive and board\-level dashboards translating platform performance, adoption, and cost into business value narratives, and mentor technical and business teams across IT, AI Advisory, and service lines to build platform fluency firmwide.
Basic Qualifications:
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- Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field
- 10\+ years of experience in technology strategy, enterprise architecture, or platform/product leadership including experience as a Chief Architect or Enterprise Solutions Architect with a demonstrated record of scaling platforms across multiple business units
- 5\+ years architecting enterprise\-scale solutions on Microsoft Azure including AI Foundry, Azure OpenAI, Cosmos DB, ADLS, Purview, and AI Search with direct accountability for platform reliability and cost at production scale
- 3\+ years leading LLM benchmarking, evaluation, and optimization programs in production environments, with a track record of translating model performance data into platform\-wide selection and routing strategy
Preferred or Desired Qualifications
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- Master's degree preferred (Computer Science, AI/ML, Cloud Architecture, or MBA with technical focus)
- Azure Solutions Architect Expert, Azure AI Engineer Associate, or equivalent certifications strongly preferred
- Data Platforms: ADLS Gen2, Cosmos DB, Azure SQL, Azure Synapse/Databricks for analytics and data engineering
- AI Search \& RAG: Vector search, semantic ranking, knowledge mining, and retrieval pipeline optimization
- Governance \& Compliance Tooling: Microsoft Purview, Azure Policy, data lineage, and audit logging frameworks
- Monitoring \& Observability: Azure Monitor, Application Insights, Log Analytics, custom dashboards (Power BI, Grafana)
- Programming Languages: Python, C\#, TypeScript, SQL, Bicep/Terraform
- DevOps \& MLOps: Azure DevOps, GitHub Actions, Docker, Kubernetes, CI/CD pipeline design
- Cost Management: Azure Cost Management, FinOps practices, token\-level cost tracking and optimization
- Proven track record of designing and implementing data governance frameworks and compliance controls at enterprise scale
- Experience managing cloud cost optimization initiatives with demonstrated savings outcomes
- Demonstrated experience mentoring technical and non\-technical teams across organizational boundaries, and experience owning vendor or model portfolio strategy in a multi\-LLM environment
- Experience establishing or scaling a platform roadmap and adoption model from an engineering\-first starting point
- Track record of leading through ambiguous, rapidly evolving technology landscapes
- Strong stakeholder management across engineering, governance, and business\-line functions
- Demonstrated experience translating governance, risk, or compliance requirements into platform\-level controls or operational practice
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa for this position now or in the future.
EisnerAmper is proud to be a merit\-based employer. We do not discriminate on the basis of veteran or disability status or any protected characteristics under federal, state, or local law.
About EisnerAmper:
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EisnerAmper is one of the largest accounting, tax, and business advisory firms, with approximately 450 partners and 4,500 employees across the world. We combine responsiveness with a long\-range perspective; to help clients meet the pressing issues they face today and position them for success tomorrow.
Our clients represent enterprises of every form, ranging from sophisticated financial institutions to startups, global public firms to middle\-market companies, governmental entities as well as high\-net\-worth individuals, family offices, nonprofit organizations and entrepreneurial ventures across a variety of industries. We are also engaged by the attorneys, financial professionals, bankers, investors, and key stakeholders who serve these clients.
Should you need any accommodations to complete this application please email: [email protected]
\#LI\-JK3
\#LI\-Hybrid
\#LI\-Remote
Preferred Location:
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Philadelphia
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 EisnerAmper, 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.
EisnerAmper AI Hiring
EisnerAmper has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, 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
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