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About This Role
Company
Federal Reserve Bank of Richmond When you join the Federal Reserve—the nation's central bank—you’ll play a key role, collaborating with leading tech professionals to strengthen and protect our economic, financial and payments systems. We invest in contemporary and emerging technology each year to support the Federal Reserve and our economy, and we’re building a dynamic and diverse team for our future.
We invite applications for AI Solutions Engineer - Associate
The AI Solutions Associate Engineer role is ideal for early-career professionals looking to build a career in supporting the development and maintenance of AI/ML solutions using Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI frameworks, combined with Infrastructure as Code and automation practices. The engineer will contribute to building and supporting scalable, secure AI-powered systems for the Federal Reserve System under the guidance of senior team members.
Infrastructure Platforms & Operations:
You will be joining The Infrastructure, Platforms & Operations portfolio. This business line supports the strategic direction and product delivery for System IT infrastructure and operations, as well as the alignment of technology to business strategies. The team serves as the business partner for secure and reliable IT products, services, and operations.
This position does not sponsor employment visas. Candidates must be U.S. citizens or lawful permanent residents with at least three years of legal residency.
There are three (3) vacancies for this job profile, candidates will be selected based on the needs of the position and the strength of the candidate pool.AI/ML Solution Development
- Work under close direction and guidance; follow established methods and policies.
- Contribute to RAG pipelines, prompt engineering, and agentic workflows to support business use cases
- Write automation scripts using Python to help streamline AI model deployment, data pipelines, and operational processes
- Assist in building and deploying GenAI, RAG, and Agentic AI solutions leveraging AWS Bedrock and related AI/ML services, under guidance from senior engineers
- Support configuration of AWS infrastructure for AI workloads including compute, storage, networking, and security using Terraform and Infrastructure as Code (IaC) principles
- Support and maintain CI/CD pipelines for automated testing, deployment, and infrastructure provisioning using GitLab or similar tools
Application Development & Support
- Assist in developing user interfaces using React for AI front end, monitoring solutions, and administrative applications
- Help troubleshoot and resolve routine to moderately complex AI infrastructure and application issues
- Code, test, debug, and document AI/ML pipeline components and infrastructure enhancements
- Produce ad-hoc queries, reports, and simple automation tools as requested by technical teams and business management
- Support system health checks and basic troubleshooting to help ensure reliability of deployed solutions
Collaboration & Process Improvement
- Work as part of an Agile team, contributing to AI capabilities that support business processes and decision-making
- Participate in design reviews and team discussions, raising questions and sharing observations
- Support change and problem management activities using standard tools, following FRIT change management policies and procedures
- Assist with analysis of business requirements as they relate to AI solutions
- Help document AI solution details, runbooks, and operational procedures following technical writing best practices
Minimum qualifications:
0 to 3 years of direct relevant work experience in AI/ML engineering, cloud infrastructure, DevOps, or software engineering OR a bachelor's degree in computer science, Information Technology, Engineering, or related field preferred.
Information technology Interns with a BA degree and a strong technical foundation, from their internship experience, and relevant coursework in AI/ML are encouraged to apply
What we are looking for:
- Foundational knowledge of Generative AI concepts, Large Language Models (LLMs), and prompt engineering
- Basic exposure to Retrieval-Augmented Generation (RAG) concepts and vector databases
- Basic understanding of Agentic AI concepts and orchestration patterns
- Working knowledge of Python for scripting and automation
- Basic proficiency with Terraform or similar Infrastructure as Code tools
- Familiarity with core AWS services (EC2, S3, Lambda, VPC, IAM)
- Basic understanding of CI/CD concepts and tools such as GitLab
- Basic knowledge of React for frontend development
- Understanding of version control systems (Git) and collaborative development workflows
- Basic knowledge of SQL queries and data analysis techniques
- Certification related to Information Technology or AI/ML preferred (AWS Certified Cloud Practitioner, HashiCorp Certified: Terraform Associate, AWS AI Practitioner, or similar)
Technical Competencies:
- General knowledge of cloud computing concepts and AI/ML system basics
- Basic understanding of networking fundamentals and security principles
- Willingness to learn and grow problem-solving skills to debug technical issues with guidance
- Ability to follow technical standards and engineering best practices
- Basic awareness of performance measures and metrics relevant to system health (nice to have)
Professional Competencies:
- Strong written and verbal communication skills with attention to detail
- Ability to document technical work clearly for technical and non-technical audiences
- Collaborator who is cooperative and open to feedback and mentorship
- Demonstrates enthusiasm for learning new AI technologies and staying current with industry trends
- Basic understanding of risk management principles in decision-making, particularly regarding responsible AI use
In addition to meeting the above requirements the following are nice to have but not required:
- Coursework, projects, or internship exposure to AWS Bedrock or GenAI/RAG applications
- Exposure to AI agent frameworks (e.g., LangChain, LangGraph, or similar)
- Basic knowledge of vector databases and embedding models
- Familiarity with containerization technologies (Docker, ECS, EKS)
- Exposure to API development and RESTful services
- Familiarity with Agile methodologies
- General familiarity with monitoring and observability concepts (e.g., logging, metrics, alerting, dashboards) and related tools such as CloudWatch, Grafana, or similar platforms
Additional Information:
The full salary range for the Associate Engineer job profile: $76,000-$95,800.00-$124,500.00.
The candidate’s new job title and associated pay range will be determined by the employing District, and they will be subject to the employing District’s people policies and practices.
As a Federal Reserve System National Business Line, it is standard practice to post System IT positions in multiple Districts to provide transparency and equitable opportunities. Internal candidate(s) selected for this position will remain employed by their current employing District and serve as an employee of System IT*.
- Current remote employees will not be guaranteed to keep their remote status with their employing bank. The selected candidate will reside within a reasonable commuting distance, as defined by the employing Reserve Bank, and will work full-time onsite.
- Eligible Locations for Hire: Boston, MA- New York, NY- Philadelphia, PA- Cleveland, OH- Richmond, VA- Atlanta, GA- Chicago, IL- St. Louis, MO- Minneapolis, MN- Kansas City, MO- Dallas, TX- San Francisco, CA OR
- The following Reserve Bank locations are preferred due to the concentration of System IT team members in these locations: Atlanta, Boston, Chicago, Kansas City, Minneapolis, New York, Richmond, and San Francisco. (example for limited posting locations)
Screening:
This position has additional screening requirements due to the information accessed while performing the job. These additional screenings would be initiated at the time of offer acceptance and could take up to a couple of months to be completed. You can begin work before the screening is completed; however, continued employment is contingent on acceptable screening results. The areas screened may include education/employment verification, criminal history, credit history, and reference checks.
Our total rewards program offers benefits that are the best fit for you at every stage of your career:
- Comprehensive healthcare options (Medical, Dental, and Vision)
- 401(k) match, and a fully funded pension plan
- Paid time off and holidays
- Annual educational assistance
- Professional development programs, training and conferences
- And more…
The Federal Reserve Banks are committed to equal employment opportunity for employees and job applicants in compliance with applicable law and to an environment where employees are valued for their differences.
Full Time / Part Time
Full timeRegular / Temporary
RegularJob Exempt (Yes / No)
YesJob Category
Information Technology Family GroupWork Shift
First (United States of America)The Federal Reserve Banks are committed to equal employment opportunity for employees and job applicants in compliance with applicable law and to an environment where employees are valued for their differences.
Always verify and apply to jobs on Federal Reserve System Careers (https://rb.wd5.myworkdayjobs.com/FRS) or through verified Federal Reserve Bank social media channels.
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Salary Context
This $76K-$124K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Federal Reserve Bank of Richmond, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $76K to $124K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Federal Reserve Bank of Richmond AI Hiring
Federal Reserve Bank of Richmond has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $124K - $153K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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