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About This Role
Description
Title:Senior System Implementation Consultant (AI \& Digital Transformation)
Location: Charlotte, NC
Duration: 29 W
Work Engagement: W2
Work Schedule: Hybrid 3 days in office/2 days remote
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
Summary:
In this contingent resource assignment, you may: Consult as an expert to develop or influence initiatives and resources for highly complex business and technical needs across Business Execution. Consult on the strategy and resolution of highly complex and unique challenges requiring in\-depth evaluation across multiple areas, delivering solutions that are long\-term, large\-scale and require vision, creativity, innovation, and advanced analytical and inductive thinking. Provide expertise to client senior leadership on innovative Business Execution business solutions. Strategically engage with client personnel. Required Qualifications: Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
Key Responsibilities:
- Lead end\-to\-end implementation of AI, Agentic AI, automation, and enterprise technology solutions from planning through production stabilization.
- Develop and manage project plans, deployment roadmaps, implementation schedules, governance forums, risks, dependencies, and executive reporting.
- Drive rollout and onboarding of AI\-enabled products and platforms, including Epiplex, Zenerate, Digital Adoption Platforms, and Agentic AI solutions.
- Partner with business stakeholders to identify AI, automation, and process transformation opportunities through process discovery, workflow analysis, and operational insights.
- Conduct process discovery workshops and current\-state assessments to identify opportunities for automation, workforce optimization, and AI adoption.
- Establish and manage Human\-in\-the\-Loop (HITL) programs, including review workflows, exception handling, quality controls, and governance frameworks.
- Lead training, onboarding, change management, and operational readiness activities to ensure successful adoption and business transition.
- Support User Acceptance Testing (UAT), release readiness, deployment validation, hypercare, and post\-production stabilization.
- Collaborate with Product, Engineering, Data Science, Risk, Compliance, and Operations teams to implement AI and intelligent automation solutions.
- Define, monitor, and report KPIs, adoption metrics, operational performance indicators, and business value realization measures.
- Develop executive dashboards and scorecards to measure adoption, productivity improvements, process efficiency gains, and realized business benefits.
Key Requirements:
- Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
- Experience in technology implementation, project management, consulting, operations transformation, product deployment, or related disciplines.
- Experience leading enterprise\-scale technology, AI, automation, or digital transformation initiatives.
- Experience with stakeholder management, project planning, governance, and executive communications.
- Experience managing user onboarding, training, operational readiness, and change management programs.
- Strong analytical, facilitation, problem\-solving, communication, and organizational skills.
Desired Qualifications
- Experience implementing AI, Generative AI, Agentic AI, workflow automation, or intelligent operations solutions.
- Knowledge of Large Language Models (LLMs), prompt engineering, AI orchestration, and enterprise AI adoption practices.
- Experience with Retrieval Augmented Generation (RAG) architectures, knowledge management solutions, document ingestion pipelines, and enterprise search capabilities.
- Familiarity with vector embeddings, semantic search, knowledge retrieval, and enterprise content indexing.
- Experience with process discovery, workflow mining, task mining, and business process intelligence platforms.
- Experience with Epiplex, Zenerate, digital learning platforms, simulation tools, or digital adoption solutions.
- Knowledge of banking operations, operational excellence, and business process transformation practices.
- PMP, Agile, Scrum, Lean Six Sigma, or equivalent certifications preferred.
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 Wells Fargo, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Wells Fargo AI Hiring
Wells Fargo has 19 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Software Engineer, AI Product Manager. Positions span Charlotte, NC, US, Woodbridge, NJ, US, Concord, CA, US. Compensation range: $224K - $355K.
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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