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About This Role
From the beginning, our goal was to establish an advisory firm that stands apart from the rest – one that is grounded in our Core Values and dedicated to creating a positive experience not just for our clients, but for our people too. We firmly believe in the strength of collaboration, enthusiasm, generosity, and perseverance as the driving forces behind our success. With advisory solutions spanning accounting and risk, technology\-enabled transformation, and transactions, we partner with our clients to solve today’s challenges and deliver present and future value.
Our commitment to our people has earned us numerous awards including Inc5000's Fastest Growing Companies and Glassdoor's Best Places to Work. Explore what our employees have to say about our unique culture by clicking here.
By joining our rapidly growing AI Innovation \& Transformation practice you will serve as a trusted partner to our clients. You’ll bring your first\-hand engineering experience, unique perspectives, and functional knowledge to design, deploy, and manage production\-grade agentic AI solutions for the Office of the CFO and other enterprise functions. As a Forward Deployed Engineer \& Agentic Workflow Engineer at CrossCountry Consulting you will be the tip of the spear for AI deployment at clients—embedding directly with client finance and operations teams to build, test, and iterate on agentic workflows that deliver measurable enterprise value on a compressed timeline. Operating at the intersection of software engineering, finance domain expertise, and client partnership, you will write production code alongside your clients, own the deployment of agentic solutions, and mentor junior engineers as a member of the practice team.
### What you'll do:
Client Delivery: Lead the development and delivery of services in the following areas:
- Rapid Jumpstart deployment: Deploy AI Jumpstart packages within client environments, configuring data connectors, calibrating agent logic, and validating outputs against source\-system controls within the first week of engagement.
- Agentic workflow design \& build: Architect and implement multi\-agent orchestration workflows using frameworks such as LangGraph, CrewAI, or AutoGen; design task decomposition, inter\-agent messaging, tool\-call schemas, and human\-in\-the\-loop (HITL) / human\-on\-the\-loop (HOTL) checkpoints appropriate to the risk and materiality of each workflow step.
- LLM integration \& prompt engineering: Write, version, and optimize system prompts and structured output schemas for LLM\-powered agents; implement function\-calling / tool\-use patterns against financial APIs; and fine\-tune retrieval strategies (hybrid structured \+ RAG) to maximize accuracy and auditability.
- Enterprise system integration: Build and certify MCP connectors and REST/GraphQL integrations to ERP (NetSuite, SAP, Oracle), EPM (Adaptive Planning, Anaplan), CRM (Salesforce), and HRIS (Workday) systems, ensuring data freshness, completeness, and reconciliation to source\-system controls.
Production deployment \& reliability: Package agents as containerized microservices (Docker/Kubernetes or equivalent) and configure CI/CD pipelines, environment promotion (dev staging* production), and monitoring/alerting so agents run reliably at client scale.
- Client co\-development: Work shoulder\-to\-shoulder with client finance, IT, and data teams; translate business requirements into technical agent design; facilitate working sessions; demonstrate working agents to executive stakeholders; and iterate rapidly based on feedback.
- SOX \& audit\-readiness: Instrument agents with deterministic logging, source citations, and control documentation so every agent\-generated output can be traced, validated, and presented to internal or external auditors.
- Contribute to developing and implementing firm\-approved, AI\-enabled solutions for clients, in accordance with company policies on data protection, intellectual property, and professional standards.
- Stay informed about emerging AI tools and techniques and collaborate with firm leadership to identify compliant opportunities to enhance client solutions and internal processes.
### Practice Leadership: Serve as a key leader in the AI Innovation \& Transformation practice by:
- Developing reusable accelerators—contributing battle\-tested code, workflow templates, and connector certifications back to the AI practice accelerator library to reduce deployment time on future engagements.
- Creating new delivery methodologies and service offerings that scale agentic solutions across clients and enterprise functions.
- Mentoring analysts and junior engineers on engagement teams, tracking and directing performance against objectives while encouraging continuous improvement and innovation.
- Contributing to recruiting, proposal writing, and firm\-wide AI innovation initiatives.
### What you'll bring:
- 5\+ years of software engineering experience with at least 2 years focused on AI/LLM application development, agentic systems, or intelligent automation; prior “forward deployed” or client\-embedded engineering experience strongly preferred.
- Hands\-on production experience building multi\-agent systems with LangChain/LangGraph, CrewAI, AutoGen, or equivalent frameworks; understanding of agent\-loop design, task planning, tool\-use, and memory management.
- Proficiency in Python (primary) and TypeScript/JavaScript; experience with FastAPI or equivalent for exposing agent capabilities as APIs.
- Deep understanding of LLM capabilities and limitations: prompt engineering, structured outputs, function calling, context\-window management, cost optimization, and latency tradeoffs across frontier models.
- Experience integrating enterprise source systems via APIs and MCP connectors; familiarity with ERP, EPM, EDW/Data Lakes, and CRM data schemas in a finance context is a significant plus.
- Working knowledge of cloud data platforms (Azure, GCP, or AWS) and containerization (Docker, Kubernetes); experience with vector databases (Pinecone, Weaviate, or equivalent) and RAG pipelines.
- Finance domain fluency—ability to understand and implement workflows covering month\-end close, variance analysis, revenue recognition, AR/AP, and FP\&A without requiring extensive hand\-holding from client finance teams.
- Exceptional ability to operate in ambiguous, fast\-moving client environments; a demonstrated track record of delivering working software in compressed, high\-stakes timelines.
- Clear, confident communication with both technical and non\-technical stakeholders; ability to present live\-agent demonstrations to CFOs and finance leadership.
- Continuous Learning Mindset: Openness to continuously learning and applying emerging LLM capabilities, agent frameworks, and enterprise integration patterns.
### Qualifications:
- A bachelor’s degree from an accredited university in computer science, software engineering,
mathematics, or a related technical discipline.
- Relevant certifications in cloud platforms (Azure AI Engineer, AWS Machine Learning Specialty,
GCP Professional ML Engineer), LangChain, or equivalent agentic AI tooling preferred.
- Willingness to travel domestically up to 20%–40% (varies by client engagement phase).
- Availability to work on client site or in office 3 days a week, with 2 days remote (hybrid
environment).
\#LI\-JF1
\#LI\-Remote
*Benefits SummaryThe CrossCountry total rewards package includes comprehensive healthcare options, including medical, dental, and vision coverage; flexible spending accounts; and a 401(k) with company matching. Additionally, employees can take advantage of generous parental and maternity leave policies, technology stipends, and wellness reimbursement programs, all designed to support both professional growth and personal well\-being. For detailed information about benefits at CrossCountry, please visit our dedicated benefits site:* https://www.crosscountry\-consulting.com/careers/benefits/. *Equal Employment Opportunity (EEO)****CrossCountry provides equal employment opportunities (EEO) to all employees and applicants for employment and believes that respect and fair treatment are critical to creating a productive and inclusive workplace.* *As an equal opportunity employer, CrossCountry is fully committed to comply with all federal, state, and local laws and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability, pregnancy, genetics, sexual orientation, veteran status, gender identity or expression or any other protected characteristic. The company also complies with pay transparency and labor laws applicable to all terms and conditions of employment.*We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 CrossCountry Consulting, 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.
CrossCountry Consulting AI Hiring
CrossCountry Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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