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About This Role
Title: Senior AI Engineer - Generative AI & Intelligent Automation
Location: 300 South Brevard Street, Charlotte, NC
Duration: 18 months (Potential to extend and/or convert)
Work Engagement: W2
Work Schedule: 3 days in office /2 days remote
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
We are seeking a highly skilled Senior AI Engineer to join a team driving enterprise-scale automation initiatives within Commercial Banking and Corporate & Investment Banking operations. This role focuses on building next-generation Generative AI, Agentic AI, and Intelligent Document Processing (IDP) solutions that automate complex customer requests and operational workflows.
The team is on a mission to transform approximately 400 manual request types into intelligent, agent-driven processes using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and workflow orchestration. This is an excellent opportunity for someone who has hands-on experience designing and implementing production-grade GenAI solutions rather than purely researching AI concepts.
What You'll Do
- Design, develop, and deploy enterprise-grade Generative AI applications and intelligent automation solutions.
- Build and maintain AI agents using Python, LangGraph, LangChain, Google ADK, and agent-based architectures.
- Develop and optimize RAG (Retrieval-Augmented Generation) solutions for document understanding and knowledge retrieval.
- Create intelligent document processing workflows capable of extracting, validating, and enriching information from a variety of document types.
- Implement OCR, PDF parsing, semantic matching, fuzzy comparison, confidence scoring, and rule-based validation capabilities.
- Develop API-driven integrations with internal platforms and operational systems.
- Build agent-driven workflows that:
+ Interpret customer intent using ML models. + Gather information from multiple enterprise applications. + Validate customer identities and business data. + Execute automated business processes. + Support KYC and compliance-related workflows. + Automatically resolve and close operational requests.
- Collaborate closely with product owners, architects, business stakeholders, and engineering teams to deliver scalable AI solutions.
- Ensure solutions are scalable, secure, highly available, and production-ready within a containerized cloud environment.
Required Qualifications
Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
- 3+ years of experience delivering solutions in Artificial Intelligence (AI), Machine Learning (ML), Generative AI, Intelligent Automation, or related fields.
- 3+ years of hands-on Python development experience.
- Strong experience with:
+ LangGraph + LangChain + Google ADK + Agent-based architectures + AI workflow orchestration
- Proven experience designing and deploying:
+ LLM-powered applications + AI Agents + RAG solutions + Intelligent automation platforms
- Extensive experience implementing Document AI / Intelligent Document Processing (IDP) solutions including:
+ OCR + PDF parsing + Document extraction + Data classification + Confidence scoring + Rule-based validation + Semantic matching + Fuzzy comparison + API integrations
- Experience building and consuming REST APIs.
- Hands-on experience working with cloud-based AI and GenAI services.
- Experience working within highly containerized environments.
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration skills.
Preferred Qualifications
- Experience within financial services, banking, or highly regulated industries.
- Experience with:
+ MongoDB + Kafka + Java-based services and APIs + Kubernetes and container orchestration platforms
- Knowledge of KYC, customer onboarding, compliance, or operational process automation.
- Experience integrating AI solutions with enterprise workflow systems.
Technical Environment
- Python
- LangGraph
- LangChain
- Google ADK
- Generative AI / LLM Platforms
- Retrieval-Augmented Generation (RAG)
- MongoDB
- Kafka
- REST APIs
- Java Services
- OCR & Document Extraction Technologies
- Cloud-Native AI Services
- Containerized Microservices Architecture
What Makes Someone Successful in This Role
The ideal candidate has real-world implementation experience building AI-powered solutions in production environments. You should be comfortable discussing specific examples where you've:
- Built and deployed RAG solutions.
- Developed AI agents using LangGraph and Python.
- Automated document-processing workflows.
- Extracted and validated data from unstructured documents.
- Integrated LLMs with APIs, databases, and enterprise applications.
- Delivered measurable operational efficiencies through AI-driven automation.
This is a hands-on engineering role for someone passionate about building intelligent systems that solve complex business problems at scale.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Wells Fargo AI Hiring
Wells Fargo has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer. Positions span Wilmington, CA, US, Charlotte, NC, US, San Francisco, CA, US. Compensation range: $254K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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