Senior Software Engineer

KMM TechnologiesColumbia, MD
Hybrid

About The Position

The client needs an experienced SDET/Automation Engineer who specializes in validating AI/ML and Generative AI systems, including testing LLM outputs, prompts, and RAG implementations. Ideal candidate will have strong technical expertise including writing complex SQL queries, automation (Selenium/BDD), and hands-on experience with cloud platforms - specifically AWS (SageMaker/Bedrock) and the Salesforce ecosystem. The candidate must have a background in the healthcare claims industry and understand critical AI challenges like model drift, hallucination detection, and bias evaluation. This role involves collaborating with cross-functional teams to build test strategies, monitor chatbots, and ensure quality delivery during a Snowflake-to-AWS migration. Excellent communication skills and adaptability are essential to clearly articulate AI testing findings to diverse stakeholders and keep up with evolving Gen AI tools.

Requirements

  • SDET/Automation Engineer background.
  • Mandatory healthcare claims industry experience.
  • Deep expertise in testing AI/ML and Gen AI systems (LLMs, RAG, prompt engineering).
  • Strong AWS (SageMaker/Bedrock) skills.
  • Salesforce skills.
  • SQL/automation skills.
  • Excellent communication skills.
  • Adaptability.

Nice To Haves

  • Experience with Databricks.
  • Experience with Snowflake.
  • Experience with CloudBees.
  • Experience with Jenkins.

Responsibilities

  • Validating AI/ML and Generative AI systems, including testing LLM outputs, prompts, and RAG implementations.
  • Writing complex SQL queries.
  • Automation testing (Selenium/BDD).
  • Hands-on experience with cloud platforms - specifically AWS (SageMaker/Bedrock) and the Salesforce ecosystem.
  • Understanding critical AI challenges like model drift, hallucination detection, and bias evaluation.
  • Collaborating with cross-functional teams to build test strategies.
  • Monitoring chatbots.
  • Ensuring quality delivery during a Snowflake-to-AWS migration.
  • Clearly articulating AI testing findings to diverse stakeholders.
  • Keeping up with evolving Gen AI tools.
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