Role Summary: As a Senior AI Engineer, you will lead the design, implementation, and operationalization of responsible AI, safety, quality, evaluation, and governance practices across GenAI and agentic AI solutions. This role sits at the intersection of AI engineering, quality engineering, governance, and production operations, ensuring that AI systems are safe, reliable, auditable, and fit for enterprise use. You will work closely with AI engineers, architects, and product owners to embed responsible AI controls into the full product lifecycle.
Key Responsibilities:
Lead the design and implementation of responsible AI, safety, and quality engineering practices for GenAI and agentic AI products.
Define and operationalize AI validation strategies covering functional correctness, hallucination risk, retrieval quality, and prompt safety.
Establish test approaches for LLM-powered applications, RAG systems, and multi-step agentic workflows.
Design evaluation frameworks for relevance, safety, groundedness, latency, and business outcome alignment.
Drive governance and auditability practices, including automated test scripts and traceability.
Partner with platform teams to implement AI observability, logging, and incident diagnostics.
Mentor engineers on AI testing, safety validation, and production monitoring.
Required Qualifications
5+ years of experience in software engineering, AI engineering, quality engineering, or AI governance.
Proven experience implementing quality, safety, and governance practices for AI-enabled systems.
Strong understanding of GenAI, agentic AI, LLM workflows, RAG, and prompt engineering.
Hands-on familiarity with enterprise AI governance frameworks (e.g., NIST AI RMF) and fairness/bias evaluation tools (e.g., Fairlearn, AIF360).
Experience with LLM evaluation and tracing tools (e.g., LangSmith, TruLens, DeepEval) and runtime guardrails (e.g., Guardrails AI, NeMo Guardrails).
Proficiency with enterprise AI monitoring and deployment platforms (e.g., Azure AI Content Safety, MLflow, Vertex AI).
Strong SDLC ownership, including test automation, CI/CD, and Python-based engineering delivery.
Preferred Qualifications
Experience testing agentic workflows involving multi-step reasoning and tool orchestration.
Familiarity with vector search platforms (e.g., Pinecone, Weaviate, FAISS) and RAG evaluation.
Experience in a build-own-operate product model.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Operationalizing Responsible AI Governance
The role requires moving beyond theoretical policy to implementing concrete engineering controls. You must demonstrate how to translate frameworks like NIST AI RMF into automated test scripts, traceability mechanisms, and release-readiness checkpoints within the SDLC.
Validation of Agentic and RAG Workflows
A core priority is establishing rigorous testing strategies for non-deterministic systems. This involves designing evaluation frameworks for hallucination, retrieval quality, and multi-step agentic reasoning, ensuring that autonomous tool orchestration remains reliable and safe.
Embedding AI Observability and Diagnostics
You must partner with platform teams to integrate logging, trace analysis, and incident diagnostics. The goal is to ensure that production AI systems are auditable and that performance metrics—such as latency and token usage—are monitored alongside business outcome alignment.
Likely challenges to think through
Balancing Innovation Speed with Risk Management
A significant challenge is the tension between rapid AI development and the necessity for strict safety guardrails. Prepare to discuss how you negotiate with product owners to implement 'governance-by-design' without stifling the velocity of AI product delivery.
Managing Non-Deterministic Failure Modes
Agentic AI and LLM-based systems present unique, unpredictable failure modes. You will need to explain how you design fallback strategies, human-in-the-loop escalation models, and adversarial testing (red-teaming) to mitigate risks in autonomous decision-making processes.
03
Practice your answers
Technical practice
Designing Evaluation Frameworks for RAG Systems
What to coverExplain how you would measure retrieval quality and groundedness in a RAG pipeline. Discuss the trade-offs between using LLM-as-a-judge versus deterministic metrics, and how you would integrate these into a CI/CD pipeline.
Operationalizing AI Guardrails
What to coverDescribe your approach to implementing runtime guardrails for agentic AI. Focus on how you balance safety and latency, and how you would handle escalation paths when a guardrail is triggered.
Validating Agentic Workflows
What to coverHow do you approach testing multi-step reasoning and tool orchestration in agentic systems? Suggest strategies for managing state, context integrity, and failure handling during autonomous tool execution.
Implementing AI Observability
What to coverWhat metrics and telemetry are essential for monitoring GenAI applications in production? Discuss how you would use tools like OpenTelemetry or Azure Monitor to diagnose hallucinations or performance regressions.
Applying NIST AI RMF in Engineering
What to coverHow do you translate high-level AI governance frameworks like NIST AI RMF into concrete engineering controls? Provide examples of how you would document traceability and auditability for model decisions.
Adversarial Testing and Red-Teaming
What to coverDescribe your methodology for conducting red-teaming exercises on LLM-powered applications. Explain how you would automate these tests to prevent regressions in prompt safety.
Fairness and Bias Mitigation
What to coverDiscuss your experience with tools like Fairlearn or AIF360. How do you identify and mitigate bias in enterprise AI systems, and how do you determine when a model is 'fair enough' for deployment?
Managing the Prompt Lifecycle
What to coverWhat engineering standards would you implement for prompt versioning and evaluation? Explain how you would ensure that prompt changes do not negatively impact existing business outcomes.
Behavioural practice
Balancing Innovation and Risk
What to coverDescribe a situation where you had to convince stakeholders to delay a release due to AI safety or quality concerns. How did you communicate the technical risk while maintaining project momentum?
Mentoring on AI Quality
What to coverProvide an example of how you mentored a team on adopting new AI testing or governance practices. How did you handle resistance to changing established development workflows?
Cross-Functional Collaboration
What to coverTell me about a time you worked with product owners and architects to define AI requirements. How did you ensure that responsible AI controls were embedded early in the design phase?
Handling Production Incidents
What to coverDescribe a time you had to diagnose a complex failure in an AI-enabled system. What was your process for identifying the root cause, and what steps did you take to prevent recurrence?
02
Sharpen your knowledge
Topics to prepare
Responsible AI and Governance Frameworks
Focus on the NIST AI Risk Management Framework (RMF) and how to translate high-level governance principles into concrete engineering controls. Exercise: Map a specific LLM failure mode, such as hallucination or bias, to a NIST RMF category and define a corresponding automated validation check.
Agentic AI Workflow Reliability
Understand the unique failure modes of multi-step agentic systems, including tool-calling errors, state management, and context loss. Exercise: Design a test plan for an agent that uses a search tool, focusing on how to validate the agent's reasoning path and its ability to handle tool execution failures.
RAG Evaluation Metrics
Master the evaluation of retrieval-augmented generation systems, specifically focusing on groundedness, relevance, and retrieval quality. Exercise: Define a set of metrics to evaluate a RAG pipeline and explain how you would distinguish between a retrieval failure and a generation failure during root cause analysis.
Skills to revise
LLM Evaluation and Tracing
Gain hands-on experience with tools like LangSmith, TruLens, or DeepEval to monitor and evaluate LLM outputs. Exercise: Create a small Python script using one of these frameworks to evaluate a prompt's output against a set of ground-truth answers for groundedness.
Runtime Guardrails Implementation
Learn to implement runtime safeguards using tools like NeMo Guardrails or Guardrails AI to constrain model behavior. Exercise: Configure a simple guardrail to prevent an LLM from discussing sensitive topics or to enforce a specific output format, and test it against adversarial inputs.
Automated AI Testing with Python
Develop automated test suites for AI systems using Python and pytest. Exercise: Write a pytest-based test harness that executes a series of prompts against an API endpoint and asserts that the responses meet predefined safety and quality criteria.
04
Your preparation checklist
Develop an AI Evaluation Framework Portfolio
Prepare a technical walkthrough of an evaluation framework you have built or managed. Focus on how you measured groundedness, hallucination rates, and retrieval quality in a RAG system, using tools like LangSmith, TruLens, or DeepEval.
Map Experience to NIST AI RMF
Create a summary document mapping your past projects to the NIST AI Risk Management Framework. Be prepared to explain how you translated high-level governance principles into concrete engineering controls like automated test scripts or guardrails.
Demonstrate Agentic Workflow Testing
Prepare a case study on testing multi-step agentic workflows. Detail your approach to validating tool-calling reliability, state management, and failure handling in autonomous or semi-autonomous AI systems.
Prepare for Technical Discussion on Guardrails
Review your experience implementing runtime safeguards. Be ready to discuss the trade-offs between latency and safety when using tools like NeMo Guardrails or Azure AI Content Safety in a production environment.
Formulate Strategic Questions for the Interview
Prepare questions regarding Ecolab's current AI lifecycle, such as: How does the team balance innovation speed with enterprise auditability requirements? What are the primary challenges currently faced in monitoring agentic AI performance in production?
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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