RiskSpan Careers

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.

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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.

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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.