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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