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Open nowPosted today

Principal AI Engineer

Ecolab914 open roles

Where
IND - Karnataka - Bangalore - EDC
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Your applicationOpen nowPrincipal AI EngineerEcolab · IND - Karnataka - Bangalore - EDC

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. Ecolab postings stay open a median of 14 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.8%30 days
  6. 59.0%60 days
This job: posted today

Ecolab median: 14 days open

The posting

Principal AI Engineer

As a Principal AI Engineer, you will lead the design and evolution of Ecolab’s GenAI and agentic AI systems at a strategic level. You will set architectural direction, establish AI governance protocols (including security, context control, and responsible use), and influence how Ecolab integrates advanced AI across its digital ecosystem. This role is ideal for deep technical experts who also bring system thinking, hands-on delivery experience, and strong cross-functional influence.

Core Responsibilities

• Define enterprise-wide architecture for GenAI systems, agent frameworks, LLM integration, and multi-agent orchestration

• Establish AI governance standards — including security, ethical usage, context management (MCP), agent-to-agent protocols (A2A), and explainability

• Lead evaluation and integration of emerging AI tooling and frameworks, from retrieval infrastructure to observability platforms

• Serve as a cross-functional advisor on LLM implementation across product teams, engineering pods, and data science

• Develop reusable components, SDKs, or templates that standardize GenAI deployment patterns

• Review and challenge architectural proposals from Lead Engineers and ensure alignment with long-term AI strategy

• Build and drive internal capability around GenAI innovation, including mentoring, tech talks, and hackathons

• Support cross-region scalability, data sensitivity considerations, and deployment performance benchmarking

• Stay abreast of industry trends and translate them into actionable design or investment recommendations

Required Skills:

• 8+ years of experience in AI/ML system design, with solid implementation in GenAI, LLMs, or agentic architectures

• Proven experience in architecting production-grade GenAI platforms or frameworks at scale

• Deep proficiency in Python, with mastery over GenAI libraries: OpenAI, transformers, Pydantic, Lang chain, faiss, pinecone, etc.

• Demonstrated experience integrating AI into cloud environments (Azure preferred: Databricks, Azure Foundry)

• Strong knowledge of AI security, privacy frameworks, hallucination mitigation, and governance

Nice-to-have skills:

• Hands-on implementation of MCP, A2A orchestration, or autonomous agent workflows

• Contributor to open-source or community-based GenAI projects

• Experience driving AI strategy across a multi-product or multi-cloud ecosystem

• Strong executive presence with the ability to translate deep tech to business impact

• Published thought leadership (papers, blogs, talks) in the AI/LLM space

• Define enterprise-wide architecture for GenAI systems, agent frameworks, LLM integration, and multi-agent orchestration

• Establish AI governance standards — including security, ethical usage, context management (MCP), agent-to-agent protocols (A2A), and explainability

• Lead evaluation and integration of emerging AI tooling and frameworks, from retrieval infrastructure to observability platforms

• Serve as a cross-functional advisor on LLM implementation across product teams, engineering pods, and data science

• Develop reusable components, SDKs, or templates that standardize GenAI deployment patterns

• Review and challenge architectural proposals from Lead Engineers and ensure alignment with long-term AI strategy

• Build and drive internal capability around GenAI innovation, including mentoring, tech talks, and hackathons

• Support cross-region scalability, data sensitivity considerations, and deployment performance benchmarking

• Stay abreast of industry trends and translate them into actionable design or investment recommendations

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