Lead Responsible AI Engineer

ecolab; India; On-site; INR 4,000,000 - 7,500,000; Full time

Job summary

Ecolab is seeking a Lead Responsible AI Engineer to design and implement governance, safety, quality, and evaluation practices for GenAI and agentic AI in production.

You will collaborate with AI engineers, architects, and platform teams to embed guardrails, testing, observability, and risk controls across the full AI lifecycle.

This hands-on role requires deep expertise in LLMs, RAG, prompt safety, and enterprise risk management to deliver trusted, scalable AI systems.

Qualifications

• 8+ years of experience in software or AI engineering with enterprise or production systems.
• Proven ability to implement quality, safety, validation, governance, risk, compliance, and auditability in AI-enabled environments.
• Strong understanding of GenAI, agentic AI, LLM workflows, retrieval-augmented generation (RAG), prompt engineering, and context-aware behavior.
• Hands-on experience defining and operating test and evaluation strategies for AI systems including functional and non-functional testing, and regression checks.
• Familiarity with responsible AI concerns such as model safety, fairness, explainability, transparency, and data sensitivity.
• Experience with enterprise governance frameworks like NIST AI RMF and translating them into pract.

Responsibilities

• Lead the design and implementation of responsible AI, safety, and quality practices for GenAI and agentic AI products
• Define AI validation strategies covering correctness, factual reliability, and prompt safety
• Establish test approaches for LLM-powered apps, RAG systems, and agentic workflows
• Design evaluation frameworks for relevance, safety, groundedness, latency, and cost awareness
• Embed guardrails, tool-calling boundaries, and escalation pathways in production systems
• Drive governance, traceability, and evidence collection across development lifecycles
• Collaborate with product and platform teams to balance speed with enterprise risk management
• Mentor engineers on AI testing and production monitoring

Skills

AI engineering

Software engineering

Quality engineering

Test engineering

AI governance

LLM workflows

RAG pipelines

Agentic AI

Observability

Job description

ROLE SUMMARY

As a Lead Responsible AI (RAI) 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 not only functional, but also safe, reliable, auditable, and fit for enterprise use.

You will work closely with AI engineers, architects, Team Leads, product owners, integration teams, and platform teams to embed responsible AI controls into the full product lifecycle. This role requires strong understanding of LLM systems, RAG pipelines, agentic workflows, prompt safety, guardrails, testing strategies, observability, and enterprise risk considerations. It is not a purely policy or compliance role - it is a hands‑on technical leadership role that ensures AI systems can be trusted in production.

The ideal candidate will combine technical depth in AI-enabled systems, practical experience with testing and quality frameworks, and the ability to guide teams toward safe, scalable, and supportable implementation patterns.

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, factual reliability, hallucination risk, retrieval quality, prompt safety, agent behavior, tool use, and failure handling
• Establish test approaches for LLM-powered applications, RAG systems, agentic workflows, multi-step reasoning, tool orchestration, and context-sensitive AI interactions
• Design and implement evaluation frameworks for relevance, safety, groundedness, consistency, latency, token usage, and business outcome alignment
• Work with AI engineers and architects to embed guardrails, prompt controls, model usage boundaries, escalation paths, fallback strategies, and safety-oriented design patterns
• Drive adoption of governance and auditability practices across AI solution development, including development of automated test scripts, enable traceability, review checkpoints, risk controls, and evidence collection
• Partner with engineering and platform teams to implement AI observability, logging, trace analysis, usage monitoring, cost awareness, and incident diagnostics
• Review solution designs to ensure they account for responsible AI, data sensitivity, model behavior risks, compliance expectations, and operational resilience
• Guide teams on how to test and validate agentic AI systems, including state management, tool-calling reliability, context integrity, multi-agent coordination, and autonomous decision boundaries
• Contribute to the definition of engineering standards for prompt lifecycle management, evaluation automation, red‑team, adversarial testing, and regression prevention
• Collaborate with product, process, and engineering stakeholders to balance innovation speed with risk management, trust, and enterprise readiness
• Mentor engineers and quality professionals on AI testing, safety validation, responsible AI engineering practices, and production monitoring approaches
• Contribute reusable assets such as test harnesses, prompt evaluation templates, governance checklists, safety review frameworks, red-team patterns, and validation accelerators

Required Qualifications

• 8+ years of experience in software engineering, AI engineering, quality engineering, test engineering, AI governance, or related technical roles, including strong experience working with AI-enabled or ML-driven systems
• Proven experience designing or implementing quality, safety, validation, governance, risk, compliance, and auditability practices for AI-enabled systems in enterprise or production environments
• Strong understanding of GenAI and agentic AI systems, including LLM workflows, retrieval-augmented generation (RAG), prompt engineering, model / tool interaction, context-aware behavior, and the failure modes associated with autonomous or semi-autonomous AI systems
• Practical knowledge of agentic AI patterns, including orchestration workflows, tool‑calling behavior, context management, memory / state handling, and multi‑step AI interactions
• Experience defining and operationalizing test and evaluation strategies for AI systems, including functional testing, non‑functional testing, hallucination analysis, retrieval validation, regression testing, groundedness evaluation, red‑team, and scenario‑based validation
• Strong understanding of responsible AI concerns, including model safety, fairness, explainability, transparency, content controls, enterprise risk management, data sensitivity, human oversight, and governance‑by‑design
• Hands‑on familiarity with enterprise responsible AI and governance frameworks such as NIST AI RMF and related GenAI / AI risk management approaches, and the ability to translate such frameworks into pract