Role

GenAI / Agentic AI Evaluation Engineer (Quality, Safety & Reliability)

Position Details

As part of EY GDS Assurance Digital, you will help design, build, and scale a standardized evaluation capability, focused on evaluating GenAI, RAG-based, and Agentic AI solutions before deployment.

This role sits at the intersection of AI evaluation engineering, Responsible AI, and GenAI security/red teaming. The primary objective is to ensure GenAI/agentic systems are protected, reliable, robust, and fit-for-purpose, by designing evaluation strategies, building repeatable test harnesses, and generating auditable evidence that supports go/no-go decisions.

You will work with global stakeholders (product teams, solution architects, risk & compliance, and assurance leadership) to define evaluation requirements, request test datasets from product teams, execute rigorous evaluations (functional + non-functional), and recommend mitigations and controls to reduce risk.

This is a core full-time role that requires a hands-on AI Development mindset, strong evaluation mindset, and the ability to translate risk concerns into practical testing strategies and measurable acceptance criteria.

Responsibilities

- Define and operationalize evaluation strategies for GenAI systems across use cases like Q&A; assistants, summarization, extraction, drafting, agentic systems, and multi-step workflows.
- Translate business use-cases into a structured evaluation plan: scope, assumptions, success criteria, datasets, metrics, red-team scenarios, thresholds, and reporting requirements.
- Drive standardization: reusable evaluation templates, test case libraries, scoring rubrics, and reporting formats across product teams.
- Design structured dataset requirements for product teams and ensure coverage across:
- Core user journeys and primary business intents
- Edge cases (rare prompts, ambiguous queries, incomplete context)
- Adversarial cases (malicious prompts, jailbreak attempts, prompt injections)
- Bias & fairness cases (sensitive demographic proxies, protected attributes, stereotyping patterns)





- Define guidance for dataset sufficiency and statistical coverage (e.g., minimum samples, distribution balance, scenario matrices, stratification by intent/risk).
- Build reusable evaluation pipelines for:

- Answer quality (correctness, relevance, completeness, clarity)
- Grounding & faithfulness (RAG-specific: faithfulness, context precision/recall, hallucination rate, citation quality)
- Agentic behavior (tool-call accuracy, tool misuse, goal completion, step correctness, unnecessary actions, loop detection, safety of tool outputs)
- Operational quality (latency, cost/token budget, throughput, stability, retries, failure recovery)

- Combine LLM-as-judge and human evaluation in a calibrated way (rubric design, sampling plans, agreement checks).
- Implement automated evaluation harnesses in Python (preferred), enabling:

- batch runs on scenario suites
- configurable metric definitions
- reproducible runs with run IDs and artifacts
- storage of traces and outputs for auditability

- Execute structured red teaming aligned to OWASP Top 10 for LLM Applications, covering (examples):

- Prompt injection (direct + indirect) and tool hijacking
- Sensitive data disclosure / PII leakage
- Insecure output handling (downstream injection)
- Training data leakage / memorization probes
- Model denial-of-service / denial-of-wallet patterns

- Integrate evals into development lifecycle: pre-release regression gates, CI checks, benchmark comparisons across model versions / prompts / tools / retrievers.
- Perform adversarial testing for agentic workflows:

- tool misuse / over-permissioned tool access
- unauthorized action execution
- exfiltration via tools/connectors




- prompt injection via retrieved documents (RAG poisoning)

- Recommend mitigations: input validation, retrieval filtering, tool sandboxing, least-privilege permissions, guardrails, policy prompting, refusal logic, output encoding, monitoring alerts.
- Produce high-quality evaluation reports that are auditable and decision-ready, including:

- methodology, datasets, metrics, thresholds
- quantitative results
- qualitative results
- risk assessment summary and recommended control actions

- Present findings to stakeholders in a crisp, risk-informed manner; clearly explain residual risk, limitations, and rationale for go/no-go.

Key Requirements/Skills & Qualification
- Excellent academic background, including at a minimum a bachelor's or a master's degree in data science, Statistics, Engineering, Operational Research, or other related field with strong focus on modern data architectures, processes, and environments.
- 4-7+ years of relevant experience in one or more areas:
- ML/AI/GenAI/Agentic engineering (NLP/LLMs), evaluation engineering, applied research
- security testing / red teaming
- building and designing evaluation harness that ensures safety, reliability and robustness.

- Strong hands-on Python for building evaluation harnesses (data processing, metric computation, orchestration, reporting pipelines).
- Practical understanding of GenAI system architectures: RAG, embeddings/vector search, prompt orchestration, tool calling, multi-agent systems, memory, routing.
- Experience designing metrics and evaluation methods (rubrics, automated scoring, sampling strategy, regression design).
- Familiarity with LLM risks and mitigations, especially for enterprise contexts (data leakage, hallucinations, prompt injection, unsafe content, bias).

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Agentic AI Evaluation Engineer (Kolkata)
🏢 EY
📍 Kolkata