Principal AI Engineer
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Job Location:
Monthly Salary:
Posted on:
6 hours ago
Vacancies:
1 Vacancy
Job Summary
Principal AI Engineer:
As a Principal AI Engineer you will lead the design and evolution of Ecolabs 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
Additional Software Engineering Responsibilities:
Drive implementation of CI/CD pipelines automated testing and deployment strategies for GenAI workloads
Ensure robust observability through logging monitoring and tracing of AI services in production
Architect distributed systems and microservices to support scalable AI applications
Collaborate with DevOps and platform engineering teams to optimize containerization (Docker/Kubernetes) and cloud-native deployments
Establish coding standards design patterns and best practices for AI/ML engineering teams
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 LangChain 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
Proven experience in architecting production-grade GenAI platforms or frameworks at scale
Deep proficiency in Python with mastery over GenAI libraries: OpenAI transformers Pydantic LangChain 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
Additional Software Engineering Skills:
Expertise in distributed systems API design and microservices architecture
Strong background in software engineering fundamentals: data structures algorithms and design patterns
Hands-on experience with CI/CD pipelines GitOps and automated testing frameworks
Proficiency in containerization and orchestration (Docker Kubernetes) for AI workloads
Familiarity with observability tools (Prometheus Grafana ELK stack) for monitoring AI systems
Experience with performance optimization benchmarking and scaling AI services in production
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
Preferred Qualifications
Advanced degree (MS/PhD) in Computer Science Software Engineering or related field with specialization in AI/ML
Experience leading large-scale engineering teams in building AI-driven platforms and services
Strong expertise in cloud-native architectures serverless computing and hybrid/multi-cloud deployments
Track record of delivering enterprise-grade AI solutions with high availability fault tolerance and disaster recovery planning
Contributions to open-source AI/ML frameworks or standards bodies
Demonstrated ability to align AI engineering practices with business strategy and measurable outcomes
Experience with compliance frameworks (GDPR HIPAA SOC2) in AI system design
Ability to mentor senior engineers and influence executive stakeholders on AI adoption strategies
Strong background in software engineering fundamentals: data structures algorithms and design patterns
Hands-on experience with CI/CD pipelines GitOps and automated testing frameworks
Proficiency in containerization and orchestration (Docker Kubernetes) for AI workloads
Familiarity with observability tools (Prometheus Grafana ELK stack) for monitoring AI systems
Experience with performance optimization benchmarking and scaling AI services in production
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
Preferred Qualifications
Advanced degree (MS/PhD) in Computer Science Software Engineering or related field with specialization in AI/ML
Experience leading large-scale engineering teams in building AI-driven platforms and services
Strong expertise in cloud-native architectures serverless computing and hybrid/multi-cloud deployments
Track record of delivering enterprise-grade AI solutions with high availability fault tolerance and disaster recovery planning
Contributions to open-source AI/ML frameworks or standards bodies
Demonstrated ability to align AI engineering practices with business strategy and measurable outcomes
Experience with compliance frameworks (GDPR HIPAA SOC2) in AI system design
Ability to mentor senior engineers and influence executive stakeholders on AI adoption strategies
Key Skills
- Design
- Academics
- AutoCAD 3D
- Cafe
- Fabrication
- Java
