RiskSpan
AI Engineer - Financial Services
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer - Financial Services, a hybrid position with a contract length of "unknown" and a pay rate of "unknown." Key skills required include strong Python, AWS services, RAG architectures, and experience in financial services.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
July 2, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
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📄 - Contract
Unknown
-
🔒 - Security
Unknown
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📍 - Location detailed
Washington, DC
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🧠 - Skills detailed
#Infrastructure as Code (IaC) #Deployment #Lambda (AWS Lambda) #AWS Glue #SQS (Simple Queue Service) #Storage #Python #Scala #SNS (Simple Notification Service) #AI (Artificial Intelligence) #SQL (Structured Query Language) #Automated Testing #Programming #Data Analysis #Data Pipeline #Redshift #Automation #Cloud #Data Ingestion #"ETL (Extract #Transform #Load)" #Databases #S3 (Amazon Simple Storage Service) #AWS (Amazon Web Services) #Data Engineering
Role description
AI Engineer – Financial Services Remote / Hybrid
About RiskSpan
RiskSpan is a leading source of analytics, modeling, data, and risk management solutions for the Consumer and Institutional Finance industries. We serve banks, issuers of mortgage- and asset-backed securities, asset managers, servicers, and regulators with cutting-edge technology and deep domain expertise across credit, market, and operational risk.
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Position Overview We are seeking a hands-on AI Engineer to design, build, and deploy production-grade AI applications using AWS Bedrock, RAG architectures, and agent-based workflows. This role focuses on building real-world AI systems- chatbots, data analysis agents, and workflow automation solutions, integrating enterprise data and delivering scalable, reliable applications in AWS. The ideal candidate brings strong Python skills, cloud-native engineering experience, and a track record of shipping production AI systems end-to-end.
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Key Responsibilities
· Design, build, and deploy AI-powered applications including chatbots, knowledge assistants, and workflow automation agents.
· Implement end-to-end solutions covering data ingestion, transformation, prompt orchestration, model interaction, and cloud deployment.
· Integrate AI systems with internal APIs, enterprise platforms, and data pipelines.
· Design agent workflows with tool/function calling, branching logic, retries, and fallback handling.
· Implement human-in-the-loop and approval-based workflows for regulated financial use cases.
· Build multi-agent systems for validation, refinement, and complex task decomposition.
· Design and implement RAG pipelines covering chunking, embeddings, retrieval, and grounding.
· Work with structured and unstructured data using SQL, S3, and data pipeline tools.
· Leverage AWS services (S3, Glue, Redshift, Lambda, ECS, Step Functions, SQS/SNS) for storage, transformation, and orchestration.
· Monitor and improve AI systems for accuracy, latency, cost, and reliability.
· Implement structured output validation, schema enforcement, and guardrails.
· Evaluate model performance and iteratively improve grounding and output consistency.
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Required Qualifications
· Strong experience building AI applications using LLMs (e.g., AWS Bedrock or equivalent platforms).
· Hands-on experience with RAG architectures and retrieval pipelines.
· Experience with vector databases, embeddings, and semantic search.
· Demonstrated track record deploying production AI systems end-to-end — not just prototypes.
· Solid Python programming skills (required).
· Experience with core AWS services: Lambda, ECS, S3, Step Functions, SQS/SNS.
· Strong SQL skills for querying and integrating structured data.
· Experience integrating AI systems with APIs, databases, and cloud services.
· Understanding of prompt engineering, tool/function calling, and structured outputs.
· Strong problem-solving skills for building reliable systems around probabilistic AI behavior.
---
Preferred Qualifications
· Experience with AWS Bedrock AgentCore or similar agent orchestration frameworks.
· Experience building multi-agent systems or advanced agent workflows.
· Experience with AWS Glue, Redshift, EMR, or broader data engineering pipelines.
· Experience with LLM evaluation frameworks and automated testing.
· Knowledge of schema validation, guardrails, and output control techniques.
· Experience with CI/CD, containerization, and infrastructure as code.
· Background in financial services, regulated environments, or GSE/enterprise data platforms.
---
Why RiskSpan? Join a team that combines deep industry expertise with cutting-edge analytics and AI to solve our clients’ most complex challenges. At RiskSpan, we foster innovation, collaboration, and continuous growth.
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Equal Opportunity Employer RiskSpan is proud to be an Equal Opportunity/Affirmative Action employer committed to hiring a diverse workforce and sustaining an inclusive culture. Qualified candidates must be legally authorized to work in the United States on an unrestricted basis.
AI Engineer – Financial Services Remote / Hybrid
About RiskSpan
RiskSpan is a leading source of analytics, modeling, data, and risk management solutions for the Consumer and Institutional Finance industries. We serve banks, issuers of mortgage- and asset-backed securities, asset managers, servicers, and regulators with cutting-edge technology and deep domain expertise across credit, market, and operational risk.
---
Position Overview We are seeking a hands-on AI Engineer to design, build, and deploy production-grade AI applications using AWS Bedrock, RAG architectures, and agent-based workflows. This role focuses on building real-world AI systems- chatbots, data analysis agents, and workflow automation solutions, integrating enterprise data and delivering scalable, reliable applications in AWS. The ideal candidate brings strong Python skills, cloud-native engineering experience, and a track record of shipping production AI systems end-to-end.
---
Key Responsibilities
· Design, build, and deploy AI-powered applications including chatbots, knowledge assistants, and workflow automation agents.
· Implement end-to-end solutions covering data ingestion, transformation, prompt orchestration, model interaction, and cloud deployment.
· Integrate AI systems with internal APIs, enterprise platforms, and data pipelines.
· Design agent workflows with tool/function calling, branching logic, retries, and fallback handling.
· Implement human-in-the-loop and approval-based workflows for regulated financial use cases.
· Build multi-agent systems for validation, refinement, and complex task decomposition.
· Design and implement RAG pipelines covering chunking, embeddings, retrieval, and grounding.
· Work with structured and unstructured data using SQL, S3, and data pipeline tools.
· Leverage AWS services (S3, Glue, Redshift, Lambda, ECS, Step Functions, SQS/SNS) for storage, transformation, and orchestration.
· Monitor and improve AI systems for accuracy, latency, cost, and reliability.
· Implement structured output validation, schema enforcement, and guardrails.
· Evaluate model performance and iteratively improve grounding and output consistency.
---
Required Qualifications
· Strong experience building AI applications using LLMs (e.g., AWS Bedrock or equivalent platforms).
· Hands-on experience with RAG architectures and retrieval pipelines.
· Experience with vector databases, embeddings, and semantic search.
· Demonstrated track record deploying production AI systems end-to-end — not just prototypes.
· Solid Python programming skills (required).
· Experience with core AWS services: Lambda, ECS, S3, Step Functions, SQS/SNS.
· Strong SQL skills for querying and integrating structured data.
· Experience integrating AI systems with APIs, databases, and cloud services.
· Understanding of prompt engineering, tool/function calling, and structured outputs.
· Strong problem-solving skills for building reliable systems around probabilistic AI behavior.
---
Preferred Qualifications
· Experience with AWS Bedrock AgentCore or similar agent orchestration frameworks.
· Experience building multi-agent systems or advanced agent workflows.
· Experience with AWS Glue, Redshift, EMR, or broader data engineering pipelines.
· Experience with LLM evaluation frameworks and automated testing.
· Knowledge of schema validation, guardrails, and output control techniques.
· Experience with CI/CD, containerization, and infrastructure as code.
· Background in financial services, regulated environments, or GSE/enterprise data platforms.
---
Why RiskSpan? Join a team that combines deep industry expertise with cutting-edge analytics and AI to solve our clients’ most complex challenges. At RiskSpan, we foster innovation, collaboration, and continuous growth.
---
Equal Opportunity Employer RiskSpan is proud to be an Equal Opportunity/Affirmative Action employer committed to hiring a diverse workforce and sustaining an inclusive culture. Qualified candidates must be legally authorized to work in the United States on an unrestricted basis.
