Associate Lead - Testing (QA + MLOps) at quantiphi – IN KA Bengaluru | Jobyssey

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.

If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role: Lead/Associate Lead – QA + MLOps & Generative AI

Experience: 10+ years

Location: Mumbai/Bangalore (Hybrid)

Key Responsibilities:

AI/ML & GenAI Testing Strategy (AWS Ecosystem)

Define testing approaches for AI systems built on AWS services such as:

Amazon SageMaker
Amazon Bedrock
AWS Lambda
Amazon API Gateway
Amazon Kinesis
AWS Glue
Amazon S3
Amazon CloudWatch

Design validation frameworks covering:

Model accuracy & performance validation
Data drift & concept drift detection
Hallucination detection for LLMs
Prompt robustness testing
RAG validation (retrieval accuracy + grounding)
Bias & fairness validation
Safety & toxicity testing

MLOps Quality Engineering (AWS-Centric)

Validate the end-to-end ML lifecycle including:

Data ingestion & feature pipelines
Model training & hyperparameter tuning
Model versioning & registry
Deployment validation
Canary & blue/green release validation

Work with AWS-native services such as:

SageMaker Pipelines
SageMaker Model Monitor
SageMaker Feature Store
Bedrock model evaluation workflows
CloudWatch-based observability

Implement CI/CD quality gates for ML pipelines integrated with AWS DevOps tools.

GenAI & Agentic AI Testing

Define quality engineering approaches for:

LLM-based applications using Amazon Bedrock
Prompt engineering validation
Multi-agent orchestration testing
Chatbot & Voice bot conversational testing
Intent classification validation
Conversation drift & fallback validation
API contract validation for LLM integrations

Build reusable evaluation harnesses for:

BLEU / ROUGE scoring
Embedding similarity scoring
Response consistency
Safety scoring frameworks

Framework & Capability Development

Design reusable AI testing accelerators
Create AWS-aligned AI test automation frameworks (Python-first)
Develop synthetic data generation strategies
Establish AI quality scorecards
Build an internal AI QA Center of Excellence

Client Engagement & Leadership

Lead AI/ML quality strategy workshops
Perform AI risk & readiness assessments
Present quality architecture to CXOs
Drive QA transformation programs
Mentor QA teams on AWS-based AI testing
Own delivery for AI testing engagements end-to-end

Must have skills:

Testing Expertise

8–12+ years in Quality Engineering
Strong test strategy, automation & governance experience
Experience leading QA transformation initiatives
Experience building frameworks from scratch AI/ML & GenAI Expertise
Deep understanding of ML lifecycle
Experience testing ML models (NLP preferred)
Hands-on experience validating LLM applications
Strong understanding of:
Prompt engineering
RAG architecture
Embeddings
Bias & explainability AWS AI/ML Expertise
Hands-on experience with:
Amazon SageMaker (training, deployment, monitoring)
Amazon Bedrock (LLM integration & evaluation)
S3-based data pipelines
AWS IAM (security validation)
CloudWatch monitoring
Lambda & API Gateway integrations
AWS CI/CD (CodePipeline / CodeBuild preferred)

Understanding of:

Infrastructure as Code (Terraform / CloudFormation)
Observability in AI systems
Cost monitoring for ML workloads

Technical Skills

Python (mandatory)
Experience with ML libraries (Scikit-learn, TensorFlow, PyTorch)
Experience with LLM frameworks (LangChain, etc.)
API & automation testing frameworks
Git-based workflows
Leadership & Communication
Strong client-facing communication
Experience leading QA teams
Ability to create strategy decks & solution proposals
Strong stakeholder management

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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