Note: The job is a remote job and is open to candidates in USA. RiskSpan is a leading source of analytics, modeling, data, and risk management solutions for the Consumer and Institutional Finance industries. The AI Engineer will design, build, and deploy production-grade AI applications, focusing on integrating enterprise data and delivering scalable solutions in AWS.

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

Skills

• 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
• 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

Company Overview