Senior AI Analytics Engineer

Senior AI Analytics Engineer

Job Summary

Our client is building an AI Native Intelligence platform that turns enterprise data into a conversational, agentic layer the business can query directly — and that continuously finds patterns, trends, and anomalies in that data on its own. The platform's architecture spans an AWS-native ingestion and extraction pipeline, a governed semantic layer and business knowledge graph, and an intelligence layer that answers questions in plain language and surfaces what's changing, instead of relying on static reports someone has to remember to check.

We're looking for an Engineer, AI Native Analytics who wants to spend essentially all day writing code and shipping working systems — implementing the conversational AI, extraction pipelines, and pattern-detection capabilities that the Lead and Senior Director design. You'll take well-scoped pieces of the roadmap and turn them into deployed, tested, working features. If what excites you is being deep in an IDE, a notebook, or a terminal — debugging a flaky Step Functions execution, tightening a prompt until it stops hallucinating, or shaving latency off a retrieval call — this role is built for you.

What You'll Do

• Write, test, and ship production code (Python and/or TypeScript/Node.js) for the platform's extraction pipelines, Bedrock agent tools, and retrieval/analytics services.
• Implement and iterate on Bedrock Agents, RAG pipelines (Knowledge Bases / OpenSearch), natural-language-to-SQL over Athena, and knowledge-graph queries (Neptune / GraphRAG) from designs handed off by the Lead.

Build and maintain the AWS extraction pipeline

Lambda functions, Step Functions state machines, DynamoDB state tracking, and Bedrock Converse API calls that turn raw unstructured data into structured facts.

• Write and refine prompts and structured-output schemas, iterating quickly against real examples until accuracy holds up.
• Build and run evaluation scripts and golden test sets; when something fails, dig down to the specific prompt, retrieval, or data issue causing it.
• Instrument, monitor, and troubleshoot production pipelines and agents — read the logs, trace the failure, fix the root cause, ship the fix.
• Write unit and integration tests, participate in code review, and keep the codebase clean enough for someone else to pick up.
• Look for opportunities to cut cost and latency (model tiering, caching, batching) without needing a reminder.
• Pick up well-scoped chunks of the roadmap from the Lead and Senior Director and turn them into working, deployed features with minimal hand-holding.

What You'll Bring

Required

• 3–6+ years of hands-on software or data engineering experience, writing and shipping production code — this is an execution-focused role, not primarily architecture, strategy, or people management.
• Daily working proficiency in Python and/or TypeScript/Node.js on real production systems.
• Practical experience with core AWS services such as Lambda, Step Functions, S3, and DynamoDB.
• Some hands-on exposure to building with LLMs — prompting, RAG, or agent/tool-calling patterns — even from personal projects or a previous role.
• Comfort with SQL and basic data analysis (Athena, Redshift, or similar).

A genuine debugging mindset

comfortable reading logs, stack traces, and unfamiliar code to find the actual root cause rather than the first plausible one.

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Preferred

• Direct experience with Amazon Bedrock (Converse API, Knowledge Bases, Agents/AgentCore).
• Experience with a vector database (OpenSearch Serverless or similar) or a graph database (Amazon Neptune).
• Experience with Amazon Comprehend or other NLP/document-processing tooling.
• Experience with CI/CD pipelines and infrastructure-as-code (AWS CDK, Terraform, or CloudFormation).
• Experience in insurance, financial services, or another regulated industry.