Q

Quantiphi

AI-driven digital transformation and automation services

Associate Lead QA + MLOps - Multiple Teams

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • The role requires 8–12+ years of experience in Quality Engineering.
  • The candidate must have strong test strategy, automation, and governance experience.
  • The candidate must have experience leading QA transformation initiatives and building frameworks from scratch.
  • The candidate must deeply understand the machine-learning lifecycle and have experience testing machine-learning models; natural language processing is preferred.
  • The candidate must have hands-on experience validating large language model applications and a strong understanding of prompt engineering, retrieval-augmented generation architecture, embeddings, bias, and explainability.
  • The candidate must have hands-on experience with Amazon SageMaker for training, deployment, and monitoring; Amazon Bedrock for large language model integration and evaluation; S3-based data pipelines; AWS Identity and Access Management security validation; CloudWatch monitoring; and Lambda and API Gateway integrations.
  • The candidate should understand Infrastructure as Code using Terraform or CloudFormation, observability in AI systems, and cost monitoring for machine-learning workloads.
  • Python is mandatory.
  • The candidate must have experience with machine-learning libraries such as scikit-learn, TensorFlow, and PyTorch; large language model frameworks such as LangChain; API and automation testing frameworks; and Git-based workflows.
  • The candidate must have client-facing communication experience, experience leading QA teams, the ability to create strategy decks and solution proposals, and stakeholder-management experience.

Responsibilities

  • Define testing approaches for AI systems built on AWS services, including Amazon SageMaker, Amazon Bedrock, AWS Lambda, Amazon API Gateway, Amazon Kinesis, AWS Glue, Amazon S3, and Amazon CloudWatch.
  • Design validation frameworks for model accuracy and performance, data drift and concept drift detection, hallucination detection for large language models, prompt robustness testing, retrieval-augmented generation validation for retrieval accuracy and grounding, bias and fairness validation, and safety and toxicity testing.
  • Validate the end-to-end machine-learning lifecycle, including data ingestion and feature pipelines, model training and hyperparameter tuning, model versioning and registry, deployment validation, and canary and blue/green release validation.
  • Work with AWS-native services, including SageMaker Pipelines, SageMaker Model Monitor, SageMaker Feature Store, Bedrock model evaluation workflows, and CloudWatch-based observability.
  • Implement continuous-integration and continuous-delivery quality gates for machine-learning pipelines integrated with AWS DevOps tools.
  • Define quality-engineering approaches for large-language-model applications using Amazon Bedrock, prompt-engineering validation, multi-agent orchestration testing, chatbot and voice-bot conversational testing, intent-classification validation, conversation drift and fallback validation, and API contract validation for large language model integrations.
  • Build reusable evaluation harnesses for BLEU and ROUGE scoring, embedding similarity scoring, response consistency, and safety-scoring frameworks.
  • Design reusable AI testing accelerators and AWS-aligned, Python-first AI test-automation frameworks; develop synthetic-data-generation strategies; establish AI quality scorecards; and build an internal AI QA Center of Excellence.
  • Lead AI/ML quality-strategy workshops, perform AI risk and readiness assessments, present quality architecture to C-suite executives, drive QA transformation programs, mentor QA teams on AWS-based AI testing, and own end-to-end delivery for AI testing engagements.

Desired Qualifications

  • Experience testing machine-learning models using natural language processing is preferred.
  • Experience with AWS CodePipeline or CodeBuild is preferred.

About the Company

Quantiphi

AI-driven digital transformation and automation services

Quantiphi uses AI and digital engineering to help organizations transform by making products and operations smarter and safer. It partners with clients to understand their challenges and then designs and builds customized AI and automation systems—combining machine learning, data analytics, and software engineering—delivering them under long-term contracts with ongoing support, and collaborating with Google Cloud to integrate technologies. Compared with competitors, it provides end-to-end, tailored solutions across industries rather than one-off projects, backed by long-term relationships and strong technology partnerships to scale. Its goal is to embed AI into products and processes to improve customer experiences and overall efficiency.

Company size
1,001-5,000
Company stage
Series A
Total funding
$23.4M
Headquarters
Marlborough, MA
Founded
2013

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