Description
AI Engineer - Financial Services Remote / Hybrid
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.
