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Salary
≈ $17k – $50k per year (Estimated)
Location
Hybrid (Bengaluru, Mumbai, India)
Seniority
Staff · 10+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Mar 24, 2026. Quantiphi scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Quantiphi is an artificial intelligence and data engineering company founded in 2013 that builds applied machine learning systems for healthcare, financial services, retail, media and public sector clients. It works as a specialist partner to the major cloud providers, and its work spans document processing, computer vision, forecasting, conversational systems and the data platforms underneath them. Headquartered in Marlborough, Massachusetts with its largest engineering base in India, it employs several thousand people and competes with both the large systems integrators and smaller boutique data science firms.

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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