Lead Responsible AI Engineer
Ecolab Inc.2 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead the design and implementation of responsible AI, safety, and quality engineering practices for GenAI and agentic AI products.
- Define and operationalize validation, testing, and evaluation strategies covering correctness, factual reliability, hallucination risk, retrieval quality, prompt safety, agent behavior, tool use, and failure handling.
- Design evaluation frameworks for relevance, safety, groundedness, consistency, latency, token usage, and business outcome alignment.
- Implement guardrails, prompt controls, model usage boundaries, escalation paths, fallback strategies, observability, logging, trace analysis, usage monitoring, and incident diagnostics.
- Drive governance, auditability, traceability, review checkpoints, risk controls, evidence collection, and release-readiness practices.
- Guide testing of agentic systems involving state management, tool-calling reliability, context integrity, multi-agent coordination, and autonomous decision boundaries.
- Define engineering standards for prompt lifecycle management, evaluation automation, red-teaming, adversarial testing, and regression prevention.
- Mentor engineers and quality professionals and contribute reusable test harnesses, evaluation templates, governance checklists, safety review frameworks, and validation accelerators.
Requirements
- 8+ years of experience in software engineering, AI engineering, quality engineering, test engineering, AI governance, or related technical roles, including strong experience with AI-enabled or ML-driven systems.
- Proven experience implementing quality, safety, validation, governance, risk, compliance, and auditability practices for AI-enabled systems in enterprise or production environments.
- Strong understanding of GenAI, agentic AI, LLM workflows, RAG, prompt engineering, model and tool interaction, context-aware behavior, and autonomous-system failure modes.
- Experience defining and operationalizing functional and non-functional testing, hallucination analysis, retrieval validation, regression testing, groundedness evaluation, red-teaming, and scenario-based validation.
- Strong understanding of responsible AI topics including safety, fairness, explainability, transparency, content controls, enterprise risk, data sensitivity, human oversight, and governance-by-design.
- Hands-on familiarity with NIST AI RMF and related AI risk-management approaches, and the ability to translate them into engineering controls and release criteria.
- Practical experience with fairness and bias evaluation tooling such as Fairlearn or AIF360, LLM evaluation tools such as LangSmith, TruLens, or DeepEval, and guardrail tools such as Guardrails AI or NeMo Guardrails.
- Experience with AI governance, monitoring, deployment, test automation, validation frameworks, CI/CD quality controls, and tools such as Azure AI Content Safety, Azure Monitor, Application Insights, OpenTelemetry, MLflow, Azure DevOps, GitHub, Git, Python, and pytest.
- Ability to collaborate with AI engineers, architects, team leads, product teams, and governance stakeholders and communicate technical risk and production-readiness concerns.
- Preferred experience with multi-step agentic workflows, MCP, A2A patterns, RAG evaluation, retrieval and vector search quality, Azure AI Search, Pinecone, Weaviate, or FAISS.
- Preferred experience contributing reusable safety controls, evaluation accelerators, governance playbooks, review templates, and AI quality engineering frameworks.
- Preferred familiarity with token usage analytics, cost controls, prompt safety, output filtering, human-in-the-loop escalation, and build-own-operate product models.