AI Engineering Manager
Ecolab (company site, opens in a new tab)10+ yrsBengaluruOn-siteDirect career pageEst. 35–60 LPA
Posted on 24 Sept
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Job description
Structured overview of role & requirements
About This Role
- Lead and mentor a multidisciplinary AI Engineering and Data Science team delivering GenAI, agentic AI, LLM-integrated applications, and ML models with end-to-end delivery accountability from discovery through production and continuous improvement.
- Own architecture, design, and delivery decisions for scalable, secure, and supportable AI/ML systems including LLM-powered systems, retrieval-augmented generation, agent orchestration, and cloud-native services.
- Drive engineering discipline and product ownership including SDLC practices, operational readiness, team performance, delivery KPIs, and stakeholder collaboration across product, platform, security, and business teams.
Minimum Requirements
- 10+ years experience in software engineering or AI/ML engineering with strong production-grade intelligent systems delivery background.
- 4-5+ years delivering or leading AI/ML/GenAI/LLM-powered solutions in enterprise or product settings.
- Proven leadership of multidisciplinary teams responsible for LLM-powered products, agentic AI workflows, and AI-enabled applications with accountability for quality and delivery.
- Strong expertise in Python, backend engineering, ML solution delivery, cloud-native application platforms (Azure or equivalent), LLM platforms and orchestration frameworks, RAG implementations, MLOps practices, CI/CD, containerization, and operational tooling.
Ideal Candidate Profile
- Technically strong leader capable of guiding senior engineers and data scientists on AI/ML architecture, implementation trade-offs, and engineering excellence in multidisciplinary teams.
- Experienced in product-oriented environments requiring end-to-end ownership of complex GenAI and agentic AI systems with cloud-native deployment and scalability considerations.
- Skilled at integrating AI/ML solutions with enterprise systems, promoting strong SDLC discipline, and driving continuous improvement of AI product delivery and operational performance.