AI Engineer - Financial Services Hybrid

AI Engineer – Financial Services Remote / Hybrid 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. #J-18808-Ljbffr