AI Engineer – Financial Services

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.

• 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.

• AI Application Development
• Python Programming
• SQL Querying
• RAG Architecture
• Embedding Techniques
• Model Performance Evaluation
• Data Transformation
• Prompt Engineering
• Tool/Function Calling
• Structured Output Validation

• Problem‑Solving