QA Lead- AI Evaluation & Quality

National e-Governance Division; New Delhi; On-site; INR 1,500,000 - 1,900,000; Full time

Job summary

National e-Governance Division seeks a seasoned AI quality engineer to lead evaluation strategy for government‑grade ML/AI systems. You will define formal gates, build reusable evaluation harnesses, and ensure adherence to responsible AI guidelines across ministry deployments.

The role requires 5+ years in ML QA, hands-on experience with LLM/RAG evaluation, and embedding tests in CI/CD. Prior government experience is a strong plus; a structured, governance minded approach is essential.

Qualifications

• 5+ years in ML or software quality assurance or applied AI.
• Minimum 3 years in AI/ML model evaluation or ML QA.
• Hands-on experience building evaluation harnesses and automated suites.
• Experience evaluating LLM/RAG systems for hallucination and retrieval quality.
• Experience embedding evaluations into CI/CD pipelines.
• Experience in government/regulatory sectors is a strong plus.
• Desirable: directing/mentoring QA/evaluation standards.

Responsibilities

• Own the programme's AI evaluation methodology and gate in delivery lifecycle.
• Choose evaluation approaches per use case and document rationale.
• Ensure Responsible AI evaluation aligns with MeitY and IndiaAI frameworks.
• Design automated evaluation harnesses, datasets and regression suites.
• Wire evaluations into CI/CD as release gates and version data/prompts.
• Publish reusable evaluation assets to AIKosh/OpenForge for national reuse.
• Assess models with metrics and report with statistical rigor.

Skills

ML QA

Model evaluation

CI/CD integration

Evaluation governance

Python

SQL

Regulated sector experience

Education

B.Tech./B.E. or M.Tech./M.S./M.Sc. in CS/DS/AI

Advanced degree desirable (MTech/MS/PhD)

Certifications: ISTQB Foundation, CT-AI

Cloud/MLOps certification desirable

Tools

CI/CD pipelines

Python

SQL

Job description

Educational Qualification

• B.Tech./B.E. or M.Tech./M.S./M.Sc. in Computer Science, Data Science, AI/ML, or a related quantitative discipline ( Must have )
• Advanced degree (M.Tech./M.S./Ph.D.) or published/applied work in ML evaluation, NLP/LLM evaluation, or Responsible AI is highly desirable
• Certifications (Desirable): ISTQB Foundation and ISTQB Certified Tester AI Testing (CT-AI); DeepLearning.AI or equivalent ML foundations; a cloud (AWS/Azure/GCP) or MLOps certification
• Non-traditional backgrounds with demonstrable model-evaluation or ML-quality work, open-source evaluation or data-quality contributions, or competitive benchmark/Kaggle achievements will be considered in lieu of formal qualification

Experience

• 5+ years in ML or software quality assurance, test automation, data or ML engineering, or applied AI; minimum 3 years specifically in AI/ML model evaluation, LLM/Gen-AI evaluation, or ML quality assurance
• Demonstrable hands‑on experience building evaluation harnesses, benchmark/golden datasets and automated evaluation suites for ML or LLM systems
• Demonstrable experience evaluating LLM/RAG systems (hallucination, groundedness, retrieval quality) and/or agentic systems (tool‑use correctness, trajectory, task completion)
• Prior experience embedding evaluations into CI/CD pipelines and versioning datasets, prompts and experiments
• Prior experience delivering AI quality or evaluation work in government, BFSI, healthcare, or another regulated sector is a strong plus
• Prior experience directing, mentoring, or setting QA/evaluation standards across multiple teams is desirable, given the programme‑lead nature of the role

Key Responsibilities

Evaluation Strategy, Standards & Quality Gates

• Own the programmes AI evaluation methodology and make evaluation a formal gate in the delivery lifecycle defining, publishing and enforcing common metrics, acceptance thresholds and quality gates
• Select the appropriate evaluation approach per use case — offline benchmarks, human evaluation, LLM‑as‑judge, or online/production evaluation — and document the rationale
• Ensure Responsible AI evaluation is built into the standard delivery flow rather than added afterwards, aligned to the MeitY Responsible AI advisory and the IndiaAI Safe & Trusted AI framework

Evaluation Infrastructure & Reusable Harnesses

• Design and build automated evaluation harnesses, benchmark and golden datasets, and regression suites that pods reuse across ministry deployments — mirroring the programme’s build‑once, reuse‑many model
• Wire evaluations into CI/CD as release gates; version datasets, prompts and evaluation configurations so results are reproducible and audit‑ready
• Publish reusable evaluation sets, rubrics, harnesses and templates to AIKosh under standard metadata (and to OpenForge where code is shared) for national reuse

Model & Output Quality Evaluation

• Evaluate models with task‑appropriate quantitative metrics — precision/recall/F1, ROC‑AUC/PR‑AUC, calibration, MAE/RMSE and task‑specific measures — reported with statistical rigour, including uncertainty, adequate sample sizes and vigilance against metric gaming
• Evaluate AI outputs against defined quality dimensions — accuracy, groundedness, reliability, consistency, relevance and adherence to business and policy requirements — rather than pass/fail alone

Gen‑AI, LLM & RAG Evaluation

• Assess LLM and RAG systems for hallucination, factual consistency, groundedness and citation faithfulness, relevance, prompt robustness, input validation and non‑deterministic behaviour
• Measure retrieval quality and generation quality separately in RAG pipelines using RAGAS, DeepEval, TruLens or LangSmith
• Use LLM‑as‑judge where appropriate, calibrated against human labels, controlling for known judge biases (position, verbosity, self‑preference), and defaulting to human evaluation where automated judgement is unreliable

Agentic AI Evaluation

• Evaluate agents on their trajectories, not only final answers — tool selection and tool‑call correctness, multi‑step reasoning and workflow completion, agent handoffs, failure recovery, memory behaviour and end‑to‑end task‑completion accuracy
• Validate human‑in‑the‑loop controls — review, escalation, override, feedback and exception handling — wherever AI decisions require human supervision

Safety, Adversarial & Responsible AI Testing

• Under the technical guidance of the AI Safety Researcher, operationalise adversarial and red‑team findings — jailbreaks, prompt injection, indirect prompt injection, unsafe behaviour and sensitive‑data (PII) leakage — into standard, reusable test suites the pods apply, and verify guardrail effectiveness (Llama Guard, NeMo Guardrails, Guardrails AI or equivalent)
• Evaluate fairness by measuring performance across demographic, linguistic, geographic and other relevant cohorts using appropriate fairness metrics (Fairlearn, AI Fairness 360)
• Author the QA‑side safety and quality evidence — model cards, dataset sheets, evaluation logs and Responsible AI records — for approval

Data & Dataset Quality Validation

• Validate training, evaluation and inference data for missing values, duplicates, schema conformance, class imbalance, data leakage, distribution/covariate shift, feature consistency and train–serve skew, using tools such as Great Expectations, Evidently or Deepchecks
• Curate high‑quality evaluation datasets that reflect real citizen usage, edge cases and Indian‑language and regional diversity

Production Monitoring & Quality Operations

• Monitor live AI applications for quality and retrieval degradation, drift (data, concept and model), accuracy and latency regressions, token/cost behaviour, failures and safety/compliance violations
• Define alerts, thresholds and dashboards for model‑quality, latency, error‑rate, drift and compliance signals; support quality‑incident response and feed production findings back into the evaluation sets

Human Evaluation Operations

• Design evaluation rubrics and run structured human evaluation and annotation; manage annotator quality and inter‑annotator agreement; build preference/label datasets that ground and validate the automated metrics

Cross‑Pod Leadership, Mentoring & Governance

• Provide technical direction to the AI QA Engineers in the team; set shared standards and harnesses, keep pod‑level practice consistent and current, and drive adoption of sound, current evaluation techniques while screening out hype
• Turn quality evidence into decision‑ready go/no‑go recommendations for deployments, and represent AI quality in programme governance and monthly reviews
• Contribute AI quality and evaluation requirements to NeGD RFQs and RDRs, and review agency deliverables for evaluation and quality posture

Technical Competencies

• Programming & Data: Python (advanced) and SQL; pandas, NumPy, scikit‑learn; pytest for test authoring
• Evaluation & Measurement: offline benchmarks, human evaluation, LLM‑as‑judge and online/production evaluation; statistical rigour — significance testing, bootstrap confidence intervals, calibration (ECE) and inter‑annotator agreement (Cohen’s / Fleiss’ kappa)
• LLM, RAG & Agentic Evaluation: RAGAS, DeepEval, TruLens, LangSmith or Promptfoo; separate retrieval‑ and generation‑quality measurement; trajectory and tool‑use evaluation for agents
• ML Metrics: precision/recall/F1, ROC‑AUC and PR‑AUC, calibration, MAE/RMSE and task‑specific metrics
• Data & Dataset Quality: Great Expectations, Evidently, Deepchecks or TFDV; detection of leakage, drift and covariate shift, class imbalance, schema violations and train–serve skew
• Safety & Responsible AI Testing: guardrail testing (Llama Guard, NeMo Guardrails, Guardrails AI), PII‑leakage and unsafe‑output testing; fairness metrics (Fairlearn, AI Fairness 360); model cards and dataset sheets — applied under the AI Safety Researcher’s guidance
• MLOps & Observability: MLflow or Weights & Biases for experiment and dataset versioning; Prometheus, Grafana, OpenTelemetry; LLM observability (Langfuse, Arize Phoenix or equivalent); CI/CD (Jenkins, GitHub Actions)
• Test Automation (supporting): pytest as the primary automation stack; Postman or REST‑assured for API testing; Selenium or Cypress where UI validation is relevant
• Security (AI‑aware): PII/sensitive‑data exposure, access controls around AI services, model/API misuse and data leakage, in addition to conventional application‑security fundamentals (authentication/authorisation, encryption, secure APIs)
• Cloud & Platforms: AWS, Azure, GCP and IndiaAI Compute evaluation and testing environments; containerised evaluation (Docker, Kubernetes)
• Government Frameworks & Standards: IndiaAI Safe & Trusted AI framework, MeitY Responsible AI advisory, MeitY security guidelines, CERT‑In directions, DPDPA 2023
• Communication & Leadership: ability to author clear, decision‑ready evaluation and quality reports for engineering, architecture and executive audiences; to set standards and mentor pod QA engineers; and to represent AI quality in cross‑ministerial reviews

Desirable Evidence at Screening

• A built evaluation harness, automated evaluation suite, or benchmark/golden dataset for an ML or LLM system, with a verifiable repository, notebook or demonstrable artefact
• Documented model or LLM/RAG evaluation with quantitative results against a production or production‑like system
• Open‑source contribution to an ML/LLM evaluation, data‑quality, or AI‑observability project with verifiable commit history
• Prior AI evaluation or quality‑assurance engagement for a government, BFSI, healthcare or regulated‑sector client, verifiable via reference or letter of confirmation