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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.
AI Solutions Engineer - Experienced
In this role you will be responsible for developing and maintaining enterprise-scale AI/ML solutions using Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI frameworks, combined with Infrastructure as Code and automation practices. You will participate in design and implement scalable, secure, and efficient AI-powered systems supporting critical business functions across the Federal Reserve System. This position requires a moderate level of experience and proficiency in AI/ML technologies, cloud infrastructure, and DevOps practices.
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.
Why Join Us:
- Lead significant AI infrastructure initiatives at a critical national institution supporting the nation's financial system
- Work with cutting-edge Generative AI, RAG, and Agentic AI technologies alongside modern automation frameworks
- Opportunity to architect and implement enterprise-scale AI solutions with real business impact
- Mentorship opportunities with junior engineers while continuing to grow your own technical expertise
- Exposure to diverse technology stacks and complex problem domains across multiple business lines
- Collaborative environment that values innovation, continuous improvement, and public service
- Comprehensive benefits package, competitive compensation, and work-life balance
- Professional development support including training, certifications, and conference attendance
What You Will Do:
AI Solution Architecture & Engineering
- Develop conceptual, logical, and physical designs for AI/ML solutions using Terraform that support the infrastructure requirements of varying levels of technical and business application projects across AWS Cloud environments
- Design and implement complex GenAI, RAG, and Agentic AI solutions leveraging AWS Bedrock and related AI/ML services, along with supporting compute, storage, networking, and security infrastructure
- Architect RAG pipelines, vector database integrations, and multi-agent orchestration systems for production use cases
- Complete analysis of business requirements as they relate to AI solution design, and ensure traceability of the design to business requirements
- Assess testing requirements and prepare testing strategies, as well as implementation and transition plans for AI infrastructure changes
- Prepare detailed specifications and documentation from which AI infrastructure will be provisioned and maintained
CI/CD & Automation
- Design, implement, and maintain sophisticated CI/CD pipelines for automated testing, deployment, and infrastructure provisioning of AI solutions using GitLab
- Develop and maintain complex automation scripts using Python or Bash to streamline model deployment, data pipelines, and manual processes to improve operational efficiency
- Utilize available tools for automation of manual processes, contributing to increased team productivity
- Follow and ensure adherence to technical standards for AI solution design and automation techniques
Problem Resolution & System Reliability
- Perform resolution of complex AI infrastructure and application problems affecting system operations; initiate problem avoidance actions
- Analyze and revise existing system logic, AI pipeline patterns, and documentation as necessary
- Recommend solutions to minimize and/or prevent system interruption based on trend analysis and proactive system checks
- Support system health and performance reviews to help ensure reliability of deployed AI solutions
Application Development & Integration
- Develop user interfaces and internal tools using React for AI front end, monitoring solutions, and administrative applications
- Build and maintain APIs and integration services to support AI solution automation and orchestration
- Design, code, test, debug, document, and maintain moderately complex software enhancements to support AI infrastructure and pipelines
Project Leadership & Collaboration
- May indirectly lead moderately complex AI infrastructure projects and participate on technical/complex cross-functional projects
- Provide project updates and prepare management reports as required, tailored for both technical and business audiences
- Work and collaborate as part of an Agile team to develop and implement AI capabilities that enable business processes and decision-making
- Train technical staff on use of AI tools, Terraform, CI/CD pipelines, and related platforms in accordance with required standards and procedures
- Participate in design reviews, architecture discussions, and provide technical mentorship to junior engineers
Governance & Compliance
- Perform change and problem management using standard tools, following FRIT change management policies and procedures for AI infrastructure propagation to other platforms and/or environments
- Contribute to development and revision of department standards, procedures, and best practices
- Assist with monitoring compliance with internal audit requirements and Information Security Manual guidelines
- Ensure AI solution implementations adhere to security best practices, responsible AI principles, compliance requirements, and cost optimization strategies
Knowledge And Skills:
Required:
- Progressive experience building GenAI, RAG, and Agentic AI solutions, including prompt engineering, RAG pipeline design, and agent orchestration frameworks
- Hands-on experience with AWS Bedrock and related AI/ML services
- Strong programming skills in Python and Bash for automation, scripting, AI/ML tooling, and API development
- Advanced proficiency in Cloud infrastructure provisioning tools, specifically Terraform, including module development and state management
- Progressive experience using core AWS services to build and support cloud solutions leveraging architecture best practices (EC2, S3, Lambda, VPC, IAM, RDS, Systems Manager)
- Strong proficiency in CI/CD and deployment automation using GitLab pipelines
- Proficiency in React for frontend development of internal tools and administrative interfaces
- Strong understanding of version control systems (Git), branching strategies, and collaborative development workflows
- Experience with SQL and NoSQL databases, data modeling, and performance optimization
Nice to Have:
- Experience with vector databases, embedding models, and semantic search implementations
- Experience integrating advanced agentic frameworks (e.g., LangChain, LangGraph, or similar) into production systems
- Expertise in containerization technologies (Docker, ECS, EKS) and container orchestration
- Understanding of FinOps practices and cloud cost optimization
- Familiarity with security scanning tools and DevSecOps practices
- Experience with disaster recovery planning and implementation
- General familiarity with observability and monitoring practices (e.g., logging, metrics, tracing, alerting) and related tools such as CloudWatch, Grafana, or similar platforms
Qualifications:
- 3-5 years of relevant work experience in AI/ML engineering, cloud infrastructure, DevOps, or platform engineering
- Minimum bachelor's degree in computer science, Information Technology, Engineering
- Certifications related to Information Technology or AI/ML strongly preferred: + AWS Certified Solutions Architect - Associate or Professional + AWS Certified Machine Learning or AI Practitioner + HashiCorp Certified: Terraform Associate or higher + AWS Certified DevOps Engineer - Professional
Technical Competencies:
- Progressive knowledge of AI/ML system architecture, cloud computing concepts, microservices, and distributed systems
- Strong understanding of networking fundamentals, security principles, system design, and scalability considerations for AI workloads
- Advanced problem-solving skills with ability to debug complex technical issues across AI pipelines and multiple layers of the stack
- Ability to interpret and apply technical standards for programming and infrastructure design techniques
- Experience with Agile methodologies, sprint planning, and iterative development
- General awareness of performance measures, metrics, and system health indicators (nice to have)
Professional Competencies:
- Strong interpersonal, negotiation, creativity, and attention to detail skills
- Excellent oral and written communication skills tailored for the intended audience, including executives, business stakeholders, and technical teams
- Demonstrated ability to lead technical discussions and influence architecture decisions
- Proven track record of delivering AI/infrastructure projects on time and within scope
- Strong mentorship and knowledge-sharing abilities
- Understanding and application of risk management discipline in decision-making, particularly regarding responsible AI use
- Works under direction with infrequent checks; makes independent decisions based on established methods and policies
Additional Information:
- Hours: May require extended work hours for deployments, maintenance windows, or incident response
- Availability: May be subject to on-call rotation and callback as needed for production support
- Travel: Occasional travel including overnight stays may be necessary for training, conferences, or cross-site collaboration
The full salary range for the Engineer, Experienced job profile:
$94,600- $118,200- $153,700
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 $94K-$153K 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($124K) sits 41% below the category median. Disclosed range: $94K to $153K.
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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