Tcs Hiring For AI QA Engineers/Consultants

Tata Consultancy Services Limited; India; Sur place; INR 450 000 - 750 000; Plein temps

Résumé du poste

Tata Consultancy Services Limited seeks AI QA Engineers/Consultants to ensure quality, reliability, and compliance of AI/ML, GenAI, LLM, and agentic AI solutions within regulated pharma environments.

You will build test strategies, evaluate models with key metrics, design tests for agents and multi-agent workflows, and develop automated QA pipelines using Python and related tools, collaborating with data scientists, engineers, and stakeholders to deliver audit-ready results.

Qualifications

• Experience in AI/ML QA, GenAI/LLM evaluation, and model validation.
• Experience with agentic AI, multi-agent systems, and evaluation of outputs.
• Proficient in test strategy design, test cases, and acceptance criteria.
• Strong analytical and stakeholder management abilities.
• Experience with AI risk, bias, and robustness testing.

Responsabilités

• Develop and execute AI/ML model QA strategies across the lifecycle.
• Evaluate models using metrics like accuracy, precision, recall, F1, ROC/AUC.
• Test GenAI/LLM for accuracy, relevance, consistency, hallucination, and grounding.
• Design tests for AI agents and multi-agent workflows with validation.
• Build test datasets, evaluation frameworks, and automated QA pipelines.
• Use Python, APIs, scripting, and automation for testing.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, and stakeholders.
• Document results in audit-ready formats and support AI governance.

Connaissances

AI/ML model QA

GenAI / LLM Evaluation

Agentic AI & Multi-Agent Systems

Precision, Recall, F1, ROC/AUC

Test Strategy & Test Case Design

Python, APIs & Automation

LangChain, LangGraph, Langfuse

Prompt Engineering & LLM Testing

AI Risk, Bias & Robustness Testing

Model/AI Application RegressionTesting

Analytical & Stakeholder Management

Description du poste

AI QA Engineers/Consultants

Job Description

Role Overview: Responsible for ensuring the quality, reliability, performance, and compliance of AI/ML, GenAI, LLM, and Agentic AI solutions within Pfizers pharmaceutical and manufacturing environment.

Key Responsibilities

• Develop and execute AI/ML model QA and evaluation strategies across the model lifecycle.
• Evaluate ML models using Accuracy, Precision, Recall, F1, ROC/AUC, Confusion Matrix, and other relevant metrics.
• Test models across datasets, edge cases, failure scenarios, and different operating conditions.
• Perform GenAI/LLM evaluation for accuracy, relevance, consistency, hallucination, groundedness, and response quality.
• Design and execute testing for AI agents and multi-agent workflows, including orchestration, tool calling, and output validation.
• Build test datasets, evaluation frameworks, test harnesses, and automated QA pipelines.
• Use Python, APIs, scripting, and automation for AI testing and evaluation.
• Work with LangChain, LangGraph, Langfuse and similar tools for agent development, tracing, observability, and evaluation.
• Identify bias, drift, hallucination, instability, and model/application failures and perform root-cause analysis.
• Define test cases, user stories, acceptance criteria, quality thresholds, and production-readiness criteria.
• Collaborate with Data Scientists, ML Engineers, Software Engineers, Product Owners, and Business Stakeholders.
• Document test results, model limitations, risks, defects, and evaluation outcomes in an audit-ready format.
• Support Responsible AI, AI Governance, Data Integrity, and Quality-by-Design practices.

Must-Have Skills

• Machine Learning & Model Evaluation
• GenAI / LLM Evaluation
• Agentic AI & Multi-Agent Systems
• Precision, Recall, F1, ROC/AUC, Confusion Matrix
• Test Strategy & Test Case Design
• Python, APIs & Automation
• LangChain, LangGraph, Langfuse
• Prompt Engineering & LLM Testing
• AI Risk, Bias & Robustness Testing
• Model/AI Application Regression Testing
• Strong Analytical & Stakeholder Management Skills

Good to Have

• Pharmaceutical / Life Sciences experience
• GxP / CSV / Data Integrity knowledge
• Azure / AWS / GCP
• MLOps / LLMOps / ModelOps
• RAG & Vector Databases
• Responsible AI & AI Governance
• CI/CD and automated testing
• AI Red-Teaming / Adversarial Testing

Experience

5+ years in AI/ML, GenAI, Model QA/Validation, Software QA, Data Science, or Quality Engineering, preferably with experience in regulated or quality-critical environments.