AI Solutions Engineer - Experienced

CompanyFederal 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 h